diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 2cdb359d..f123ed68 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -315,6 +315,18 @@ jobs:
           python plot_compare_pygrt.py
         continue-on-error: true  # 即使失败，仍然标记为成功
 
+      - name: Test strain stress (dynamic)
+        working-directory: ${{ env.EXAMPLE_COPY_PATH }}/compute_strain_stress/dynamic
+        run: |
+          chmod +x *.sh
+          ./run_grt.sh
+
+      - name: Test static displacement (static)
+        working-directory: ${{ env.EXAMPLE_COPY_PATH }}/compute_strain_stress/static
+        run: |
+          chmod +x *.sh
+          ./run_stgrt.sh
+
       - name: Remove the test files
         run: |
           rm -rf ${{ env.EXAMPLE_COPY_PATH }}
diff --git a/.github/workflows/testbuild.yml b/.github/workflows/testbuild.yml
index 3cdfb94d..fc34a9e5 100644
--- a/.github/workflows/testbuild.yml
+++ b/.github/workflows/testbuild.yml
@@ -281,6 +281,18 @@ jobs:
           ./run_grt.sh
           python run_pygrt.py
 
+      - name: Test strain stress (dynamic)
+        working-directory: ${{ env.EXAMPLE_COPY_PATH }}/compute_strain_stress/dynamic
+        run: |
+          chmod +x *.sh
+          ./run_grt.sh
+
+      - name: Test static displacement (static)
+        working-directory: ${{ env.EXAMPLE_COPY_PATH }}/compute_strain_stress/static
+        run: |
+          chmod +x *.sh
+          ./run_stgrt.sh
+
       - name: Remove the test files
         run: |
           rm -rf ${{ env.EXAMPLE_COPY_PATH }}
diff --git a/example/check_upar/README b/example/check_upar/README
new file mode 100644
index 00000000..cfe6e1a7
--- /dev/null
+++ b/example/check_upar/README
@@ -0,0 +1 @@
+Use difference to check spatial derivatives calculation.
\ No newline at end of file
diff --git a/example/check_upar/compare_uir.pdf b/example/check_upar/compare_uir.pdf
new file mode 100644
index 00000000..92e40a9e
Binary files /dev/null and b/example/check_upar/compare_uir.pdf differ
diff --git a/example/check_upar/compare_uiz.pdf b/example/check_upar/compare_uiz.pdf
new file mode 100644
index 00000000..c94250d3
Binary files /dev/null and b/example/check_upar/compare_uiz.pdf differ
diff --git a/example/check_upar/milrow b/example/check_upar/milrow
new file mode 100644
index 00000000..a8a74b18
--- /dev/null
+++ b/example/check_upar/milrow
@@ -0,0 +1,9 @@
+0.2  3.4  1.7  2.3  9e10  9e10
+0.6  3.7  1.9  2.4  9e10  9e10
+0.5  4.2  2.1  2.4  9e10  9e10
+0.5  4.6  2.3  2.5  9e10  9e10
+0.7  4.9    2.8  2.6  9e10  9e10
+0.5  5.1  2.9  2.7  9e10  9e10
+6.0  5.9  3.3  2.7  9e10  9e10
+28.  6.9  4.0  2.8  9e10  9e10
+-0.1   8.2  4.7  3.2  9e10  9e10
diff --git a/example/check_upar/plot.sh b/example/check_upar/plot.sh
new file mode 100755
index 00000000..591e8b9e
--- /dev/null
+++ b/example/check_upar/plot.sh
@@ -0,0 +1,69 @@
+#!/bin/bash
+
+SAC_DISPLAY_COPYRIGHT=0
+
+out="compare_pdf"
+rm -rf $out
+mkdir -p $out
+
+diff=0.001
+depsrc=2
+deprcv=3.2
+deprcv2=`echo $deprcv+$diff | bc | awk '{printf("%.3f", $1)}'`
+dist=10
+dist2=`echo $dist+$diff | bc | awk '{printf("%.3f", $1)}'`
+
+GRN="GRN"
+
+# --------------------- ui_z --------------------------------------
+for ch in $(ls $GRN/milrow_${depsrc}_${deprcv2}_${dist}); do
+echo $ch
+sac <<EOF
+r $GRN/milrow_${depsrc}_${deprcv2}_${dist}/$ch
+subf $GRN/milrow_${depsrc}_${deprcv}_${dist}/$ch
+div -$diff
+w uiz_diff.sac
+r uiz_diff.sac
+r more $GRN/milrow_${depsrc}_${deprcv}_${dist}/z${ch:0:3}.sac
+int
+qdp off
+color red inc list blue red 
+p2
+saveimg $out/compare_$ch.pdf
+q
+EOF
+
+done
+
+rm uiz_diff.sac
+
+pdftk $out/* cat output compare_uiz.pdf
+
+rm -rf $out
+mkdir -p $out
+
+# --------------------- ui_z --------------------------------------
+for ch in $(ls $GRN/milrow_${depsrc}_${deprcv}_${dist2}); do
+echo $ch
+sac <<EOF
+r $GRN/milrow_${depsrc}_${deprcv}_${dist2}/$ch
+subf $GRN/milrow_${depsrc}_${deprcv}_${dist}/$ch
+div $diff
+w uir_diff.sac
+r uir_diff.sac
+r more $GRN/milrow_${depsrc}_${deprcv}_${dist}/r${ch:0:3}.sac
+int
+qdp off
+color red inc list blue red 
+p2
+saveimg $out/compare_$ch.pdf
+q
+EOF
+
+done
+
+rm uir_diff.sac
+
+pdftk $out/* cat output compare_uir.pdf
+
+rm -rf $out
\ No newline at end of file
diff --git a/example/check_upar/run_grt.sh b/example/check_upar/run_grt.sh
new file mode 100755
index 00000000..b87fda00
--- /dev/null
+++ b/example/check_upar/run_grt.sh
@@ -0,0 +1,19 @@
+#!/bin/bash 
+
+depsrc=2
+nt=1024
+dt=0.01
+
+modname="milrow"
+out="GRN"
+
+
+diff=0.001
+deprcv=3.2
+deprcv2=`echo $deprcv+$diff | bc | awk '{printf("%.3f", $1)}'`
+dist=10
+dist2=`echo $dist+$diff | bc | awk '{printf("%.3f", $1)}'`
+
+grt -M${modname} -O${out} -N${nt}/${dt} -D${depsrc}/$deprcv -R$dist -e
+grt -M${modname} -O${out} -N${nt}/${dt} -D${depsrc}/$deprcv2 -R$dist
+grt -M${modname} -O${out} -N${nt}/${dt} -D${depsrc}/$deprcv -R$dist2
diff --git a/example/compute_strain_stress/dynamic/milrow b/example/compute_strain_stress/dynamic/milrow
new file mode 100644
index 00000000..a8a74b18
--- /dev/null
+++ b/example/compute_strain_stress/dynamic/milrow
@@ -0,0 +1,9 @@
+0.2  3.4  1.7  2.3  9e10  9e10
+0.6  3.7  1.9  2.4  9e10  9e10
+0.5  4.2  2.1  2.4  9e10  9e10
+0.5  4.6  2.3  2.5  9e10  9e10
+0.7  4.9    2.8  2.6  9e10  9e10
+0.5  5.1  2.9  2.7  9e10  9e10
+6.0  5.9  3.3  2.7  9e10  9e10
+28.  6.9  4.0  2.8  9e10  9e10
+-0.1   8.2  4.7  3.2  9e10  9e10
diff --git a/example/compute_strain_stress/dynamic/run_grt.sh b/example/compute_strain_stress/dynamic/run_grt.sh
new file mode 100755
index 00000000..7696d976
--- /dev/null
+++ b/example/compute_strain_stress/dynamic/run_grt.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+
+dist=10
+depsrc=2
+deprcv=0
+
+nt=1024
+dt=0.01
+
+modname="milrow"
+out="GRN"
+
+grt -M${modname} -O${out} -N${nt}/${dt} -D${depsrc}/${deprcv} -R${dist} -e 
+
+# Fault
+S="u1e8"
+stk=77
+dip=88
+rak=99
+az=30
+grt.syn -G${out}/${modname}_${depsrc}_${deprcv}_${dist} -A$az -S$S -M$stk/$dip/$rak -Dp/0.6 -Osyn_dc -e
+
+
+grt.strain syn_dc/out
+grt.stress syn_dc/out
\ No newline at end of file
diff --git a/example/compute_strain_stress/static/milrow b/example/compute_strain_stress/static/milrow
new file mode 100644
index 00000000..a8a74b18
--- /dev/null
+++ b/example/compute_strain_stress/static/milrow
@@ -0,0 +1,9 @@
+0.2  3.4  1.7  2.3  9e10  9e10
+0.6  3.7  1.9  2.4  9e10  9e10
+0.5  4.2  2.1  2.4  9e10  9e10
+0.5  4.6  2.3  2.5  9e10  9e10
+0.7  4.9    2.8  2.6  9e10  9e10
+0.5  5.1  2.9  2.7  9e10  9e10
+6.0  5.9  3.3  2.7  9e10  9e10
+28.  6.9  4.0  2.8  9e10  9e10
+-0.1   8.2  4.7  3.2  9e10  9e10
diff --git a/example/compute_strain_stress/static/run_stgrt.sh b/example/compute_strain_stress/static/run_stgrt.sh
new file mode 100755
index 00000000..ea26cabd
--- /dev/null
+++ b/example/compute_strain_stress/static/run_stgrt.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+
+depsrc=2
+deprcv=0
+
+x1=-3
+x2=3
+nx=10
+
+y1=-3
+y2=3
+ny=10
+
+stgrt -Mmilrow -D${depsrc}/${deprcv} -X$x1/$x2/$nx -Y$y1/$y2/$ny -e > grn 
+
+# Fault
+S="u1e8"
+stk=77
+dip=88
+rak=99
+stgrt.syn -S$S -M$stk/$dip/$rak -e -N < grn > syn 
+
+stgrt.strain < syn > strain
+stgrt.stress < syn > stress
\ No newline at end of file
diff --git a/example/compute_strain_stress/static2/README b/example/compute_strain_stress/static2/README
new file mode 100644
index 00000000..6e758cd0
--- /dev/null
+++ b/example/compute_strain_stress/static2/README
@@ -0,0 +1,2 @@
+结果对比：
+    郝金来, 姚振兴, 2012. 均匀弹性分层介质模型中的同震位移、应变以及应力[J]. 地球物理学报, 55(5): 1682-1694.
diff --git a/example/compute_strain_stress/static2/halfspace2 b/example/compute_strain_stress/static2/halfspace2
new file mode 100644
index 00000000..01a24842
--- /dev/null
+++ b/example/compute_strain_stress/static2/halfspace2
@@ -0,0 +1 @@
+0.0  4.0 2.7 2.5 9e30 9e30
\ No newline at end of file
diff --git a/example/compute_strain_stress/static2/mod b/example/compute_strain_stress/static2/mod
new file mode 100644
index 00000000..31e86f44
--- /dev/null
+++ b/example/compute_strain_stress/static2/mod
@@ -0,0 +1,5 @@
+20 5.8 3.35 2.7 9e30 9e30
+20 6.8 3.90 2.8 9e30 9e30
+20 7.8 4.45 2.9 9e30 9e30
+20 8.8 5.00 3.0 9e30 9e30
+20 9.8 5.55 3.1 9e30 9e30
\ No newline at end of file
diff --git a/example/compute_strain_stress/static2/plot.ipynb b/example/compute_strain_stress/static2/plot.ipynb
new file mode 100644
index 00000000..20cf83d8
--- /dev/null
+++ b/example/compute_strain_stress/static2/plot.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5f279855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "311dcbe4",
+   "metadata": {},
+   "source": [
+    "compare displacements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fe2c0971",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1200 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dipslip1 = np.loadtxt(\"syn_1_dipslip\")\n",
+    "dipslip2 = np.loadtxt(\"syn_2_dipslip\")\n",
+    "stkslip1 = np.loadtxt(\"syn_1_stkslip\")\n",
+    "stkslip2 = np.loadtxt(\"syn_2_stkslip\")\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 3, figsize=(10,12))\n",
+    "for i, slip in enumerate([stkslip1, dipslip1, stkslip2, dipslip2]):\n",
+    "    ax3 = axs[i]\n",
+    "    chs = ['Z', 'N', 'E']\n",
+    "    for k in range(3):\n",
+    "        ax = ax3[k]\n",
+    "        y = slip[:, k+2].copy()\n",
+    "        if chs[k] == 'E':\n",
+    "            y *= -1\n",
+    "        ax.plot(slip[:, 1], y, label=chs[k])\n",
+    "        ax.grid()\n",
+    "        ax.xaxis.set_inverted(True)\n",
+    "        ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cb3a0fd",
+   "metadata": {},
+   "source": [
+    "compare stress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "622ba801",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1200 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dipslip1 = np.loadtxt(\"stress_1_dipslip\")\n",
+    "dipslip2 = np.loadtxt(\"stress_2_dipslip\")\n",
+    "stkslip1 = np.loadtxt(\"stress_1_stkslip\")\n",
+    "stkslip2 = np.loadtxt(\"stress_2_stkslip\")\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 2, figsize=(8,12))\n",
+    "for i, data in enumerate([stkslip1, dipslip1, stkslip2, dipslip2]):\n",
+    "    ax3 = axs[i]\n",
+    "\n",
+    "    ax = ax3[0]\n",
+    "    ax.plot(data[:,1], data[:, 2], label='ZZ', c='b')\n",
+    "    ax.plot(data[:,1], data[:, 5], label='NN', c='k')\n",
+    "    ax.plot(data[:,1], data[:, 7], label='EE', c='r')\n",
+    "    ax.grid()\n",
+    "    ax.xaxis.set_inverted(True)\n",
+    "    ax.legend()\n",
+    "\n",
+    "    ax = ax3[1]\n",
+    "    ax.plot(data[:,1], data[:, 3], label='ZN', c='r')\n",
+    "    ax.plot(data[:,1], -data[:, 4], label='ZE', c='b')\n",
+    "    ax.plot(data[:,1], -data[:, 6], label='NE', c='k')\n",
+    "    ax.grid()\n",
+    "    ax.legend()\n",
+    "    ax.xaxis.set_inverted(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "69f7590c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dipslip3 = np.loadtxt(\"stress_3_dipslip\")\n",
+    "stkslip3 = np.loadtxt(\"stress_3_stkslip\")\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(8,6))\n",
+    "for i, data in enumerate([stkslip3, dipslip3]):\n",
+    "    ax3 = axs[i]\n",
+    "\n",
+    "    ax = ax3[0]\n",
+    "    ax.plot(data[:,1], data[:, 2], label='ZZ', c='b')\n",
+    "    ax.plot(data[:,1], data[:, 5], label='NN', c='k')\n",
+    "    ax.plot(data[:,1], data[:, 7], label='EE', c='r')\n",
+    "    ax.grid()\n",
+    "    ax.xaxis.set_inverted(True)\n",
+    "    ax.legend()\n",
+    "\n",
+    "    ax = ax3[1]\n",
+    "    ax.plot(data[:,1], data[:, 3], label='ZN', c='r')\n",
+    "    ax.plot(data[:,1], -data[:, 4], label='ZE', c='b')\n",
+    "    ax.plot(data[:,1], -data[:, 6], label='NE', c='k')\n",
+    "    ax.grid()\n",
+    "    ax.legend()\n",
+    "    ax.xaxis.set_inverted(True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pygrt-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/example/compute_strain_stress/static2/run.sh b/example/compute_strain_stress/static2/run.sh
new file mode 100755
index 00000000..5bf39c50
--- /dev/null
+++ b/example/compute_strain_stress/static2/run.sh
@@ -0,0 +1,31 @@
+#!/bin/bash 
+
+
+mod="halfspace2"
+stgrt -M$mod -D20/16 -X3/3/1 -Y-10/10/41 -e > grn1
+
+stgrt.syn -M0/70/0 -Su1e6 -e -N < grn1 > syn_1_stkslip
+stgrt.stress < syn_1_stkslip > stress_1_stkslip
+
+stgrt.syn -M0/70/90 -Su1e6 -e -N < grn1 > syn_1_dipslip
+stgrt.stress < syn_1_dipslip > stress_1_dipslip
+
+
+
+stgrt -M$mod -D10/14 -X3/3/1 -Y-10/10/41 -e > grn2
+
+stgrt.syn -M0/70/0 -Su1e6 -e -N < grn2 > syn_2_stkslip
+stgrt.stress < syn_2_stkslip > stress_2_stkslip
+
+stgrt.syn -M0/70/90 -Su1e6 -e -N < grn2 > syn_2_dipslip
+stgrt.stress < syn_2_dipslip > stress_2_dipslip
+
+
+mod="mod"
+stgrt -M$mod -D5/0 -X3/3/1 -Y-10/10/41 -e > grn3
+
+stgrt.syn -M0/70/0 -Su1e6 -e -N < grn3 > syn_3_stkslip
+stgrt.stress < syn_3_stkslip > stress_3_stkslip
+
+stgrt.syn -M0/70/90 -Su1e6 -e -N < grn3 > syn_3_dipslip
+stgrt.stress < syn_3_dipslip > stress_3_dipslip
diff --git a/example/static_disp/disp_dc.pdf b/example/static_disp/disp_dc.pdf
new file mode 100644
index 00000000..3e6c87e8
Binary files /dev/null and b/example/static_disp/disp_dc.pdf differ
diff --git a/example/static_disp/disp_exp.pdf b/example/static_disp/disp_exp.pdf
new file mode 100644
index 00000000..7a3c19da
Binary files /dev/null and b/example/static_disp/disp_exp.pdf differ
diff --git a/example/static_disp/disp_sf.pdf b/example/static_disp/disp_sf.pdf
new file mode 100644
index 00000000..9715ec36
Binary files /dev/null and b/example/static_disp/disp_sf.pdf differ
diff --git a/example/static_disp/halfspace2 b/example/static_disp/halfspace2
new file mode 100644
index 00000000..01a24842
--- /dev/null
+++ b/example/static_disp/halfspace2
@@ -0,0 +1 @@
+0.0  4.0 2.7 2.5 9e30 9e30
\ No newline at end of file
diff --git a/example/static_disp/run_stgrt.sh b/example/static_disp/run_stgrt.sh
new file mode 100755
index 00000000..2053584f
--- /dev/null
+++ b/example/static_disp/run_stgrt.sh
@@ -0,0 +1,73 @@
+#!/bin/bash 
+
+depsrc=2
+deprcv=0
+
+x1=-3
+x2=3
+nx=20
+
+y1=-3
+y2=3
+ny=20
+
+stgrt -Mhalfspace2 -D${depsrc}/${deprcv} -X$x1/$x2/$nx -Y$y1/$y2/$ny > grn 
+
+# Fault
+S="1e23"
+stk=60
+dip=90
+rak=0
+stgrt.syn -S$S -M$stk/$dip/$rak -N < grn > syn 
+
+gmt set FONT_TITLE 9p
+gmt begin disp_dc pdf
+    gmt xyz2grd syn -GsynZ.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,2 -:
+    gmt xyz2grd syn -GsynN.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,3 -:
+    gmt xyz2grd syn -GsynE.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,4 -:
+
+    gmt basemap -Baf -BWSen+t"Fault, $stk/$dip/$rak, $S" -JX5c/5c -R$y1/$y2/$x1/$x2
+    gmt grdimage synZ.nc
+    gmt grdvector synE.nc synN.nc -Q0.1c+e+jc+h1+gblack -Si0.3c
+
+    gmt meca -Sa0.5c <<EOF
+0 0 $deprcv $stk $dip $rak 5
+EOF
+
+    gmt colorbar -Bx+l"Z (cm)"
+gmt end
+
+
+# Single Force
+S="1e17"
+fn=1
+fe=1
+fz=0
+stgrt.syn -S$S -F$fn/$fe/$fz -N < grn > syn 
+gmt begin disp_sf pdf
+    gmt xyz2grd syn -GsynZ.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,2 -:
+    gmt xyz2grd syn -GsynN.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,3 -:
+    gmt xyz2grd syn -GsynE.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,4 -:
+
+    gmt basemap -Baf -BWSen+t"Force, $fn/$fe/$fz, $S" -JX5c/5c -R$y1/$y2/$x1/$x2
+    gmt grdimage synZ.nc
+    gmt grdvector synE.nc synN.nc -Q0.1c+e+jc+h1+gblack -Si0.5c
+
+    gmt colorbar -Bx+l"Z (cm)"
+gmt end
+
+# Explosion
+S="1e23"
+stgrt.syn -S$S -N < grn > syn 
+gmt begin disp_exp pdf
+    gmt xyz2grd syn -GsynZ.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,2 -:
+    gmt xyz2grd syn -GsynN.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,3 -:
+    gmt xyz2grd syn -GsynE.nc -R$y1/$y2/$x1/$x2 -I$ny+n/$nx+n -i0,1,4 -:
+
+    gmt basemap -Baf -BWSen+t"Explosion, $S" -JX5c/5c -R$y1/$y2/$x1/$x2
+    gmt grdimage synZ.nc
+    gmt grdvector synE.nc synN.nc -Q0.1c+e+jc+h1+gblack -Si0.3c
+
+    gmt colorbar -Bx+l"Z (cm)"
+gmt end
+
diff --git a/example/view_integ_stats/README b/example/view_integ_stats/README
new file mode 100644
index 00000000..3749437f
--- /dev/null
+++ b/example/view_integ_stats/README
@@ -0,0 +1 @@
+View slow convergency when source and receiver are at close or same depth.
\ No newline at end of file
diff --git a/example/view_integ_stats/halfspace2 b/example/view_integ_stats/halfspace2
new file mode 100644
index 00000000..01a24842
--- /dev/null
+++ b/example/view_integ_stats/halfspace2
@@ -0,0 +1 @@
+0.0  4.0 2.7 2.5 9e30 9e30
\ No newline at end of file
diff --git a/example/view_integ_stats/run_stgrt.sh b/example/view_integ_stats/run_stgrt.sh
new file mode 100755
index 00000000..514c553b
--- /dev/null
+++ b/example/view_integ_stats/run_stgrt.sh
@@ -0,0 +1,4 @@
+#!/bin/bash 
+
+
+stgrt -Mhalfspace2 -D0.1/0 -X-3/3/10 -Y-5/5/20 -e -S
diff --git a/example/view_integ_stats/view.ipynb b/example/view_integ_stats/view.ipynb
new file mode 100644
index 00000000..6cadb88e
--- /dev/null
+++ b/example/view_integ_stats/view.ipynb
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "379a533f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pygrt\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "89b99900",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.10214\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x900 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mod = \"halfspace2\"\n",
+    "depsrc = \"0.10000\"\n",
+    "deprcv = \"0.00000\"\n",
+    "strdists = [p.split(\"_\")[-1] for p in glob.glob(f\"stgrtstats/{mod}_stats_{depsrc}_{deprcv}/K_*\")]\n",
+    "strdists.sort()\n",
+    "sdist = strdists[22]\n",
+    "print(sdist)\n",
+    "data = pygrt.utils.read_statsfile(f\"stgrtstats/{mod}_stats_{depsrc}_{deprcv}/K_{sdist}\")\n",
+    "\n",
+    "fig, ax = pygrt.utils.plot_statsdata(data, \"DC\", \"2\", \"w\", \"2\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5958a6f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ptamdata = pygrt.utils.read_statsfile_ptam(f\"stgrtstats/{mod}_stats_{depsrc}_{deprcv}/PTAM_{sdist}\")\n",
+    "fig, ax = pygrt.utils.plot_statsdata_ptam(data, ptamdata, \"DC\", \"2\", \"3\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pygrt-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pygrt/C_extension/Makefile b/pygrt/C_extension/Makefile
index b5fc9f13..cdd961d8 100644
--- a/pygrt/C_extension/Makefile
+++ b/pygrt/C_extension/Makefile
@@ -48,6 +48,7 @@ CFLAGS :=  $(LINK_STATIC) $(LFFT_FLAGS) -lm -O3 \
 SUBDIRS = \
     src/common \
 	src/dynamic \
+	src/static \
 	src/travt
 
 
@@ -56,12 +57,12 @@ all: objs progs libs
 
 objs:
 	for dir in $(SUBDIRS); do \
-		$(MAKE) -C $$dir CC="$(CC)" CFLAGS="$(CFLAGS)" objs; \
+		$(MAKE) -C $$dir CC="$(CC)" CFLAGS="$(CFLAGS)" objs || exit 1; \
 	done
 
 progs: objs 
 	for dir in $(SUBDIRS); do \
-		$(MAKE) -C $$dir CC="$(CC)" CFLAGS="$(CFLAGS)" progs; \
+		$(MAKE) -C $$dir CC="$(CC)" CFLAGS="$(CFLAGS)" progs || exit 1; \
 	done
 
 
diff --git a/pygrt/C_extension/include/common/const.h b/pygrt/C_extension/include/common/const.h
index bf0f75be..3c325363 100755
--- a/pygrt/C_extension/include/common/const.h
+++ b/pygrt/C_extension/include/common/const.h
@@ -72,7 +72,8 @@ typedef int MYINT;  ///< 整数
     #define FIVEQUARTERPI  3.92699082f  ///< \f$ \frac{5\pi}{4} \f$
     #define SEVENQUARTERPI  5.49778714f   ///< \f$ \frac{7\pi}{4} \f$
     #define INV_SQRT_TWO 0.70710678f   ///< \f$ \frac{1}{\sqrt{2}} \f$ 
-
+    #define DEG1 0.017453293f  ///< \f$ \frac{\pi}{180} \f$ 
+    
 #else 
     typedef double _Complex MYCOMPLEX;
     typedef double MYREAL;
@@ -109,7 +110,8 @@ typedef int MYINT;  ///< 整数
     #define FIVEQUARTERPI  3.9269908169872414  ///< \f$ \frac{5\pi}{4} \f$
     #define SEVENQUARTERPI  5.497787143782138   ///< \f$ \frac{7\pi}{4} \f$
     #define INV_SQRT_TWO 0.7071067811865475   ///< \f$ \frac{1}{\sqrt{2}} \f$ 
-
+    #define DEG1 0.017453292519943295  ///< \f$ \frac{\pi}{180} \f$ 
+    
 #endif
 
 #define CZERO CMPLX(RZERO, RZERO)  ///< 0.0 + j0.0
diff --git a/pygrt/C_extension/include/common/coord.h b/pygrt/C_extension/include/common/coord.h
new file mode 100644
index 00000000..23875142
--- /dev/null
+++ b/pygrt/C_extension/include/common/coord.h
@@ -0,0 +1,47 @@
+/**
+ * @file   coord.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-10
+ * 
+ * 关于坐标变换的一些函数
+ * 
+ */
+
+#pragma once 
+
+#include <stdbool.h>
+
+/**
+ * 直角坐标zxy到柱坐标zrt的矢量旋转
+ * 
+ * @param   theta        (in)r轴相对x轴的旋转弧度(负数表示逆变换，即zrt->zxy)
+ * @param   A[3]         (inout)待旋转的矢量(s1, s2, s3)
+ */
+void rot_zxy2zrt_vec(double theta, double A[3]);
+
+
+
+/**
+ * 直角坐标zxy到柱坐标zrt的二阶对称张量旋转
+ * 
+ * @param   theta        (in)r轴相对x轴的旋转弧度(负数表示逆变换，即zrt->zxy)
+ * @param   A[6]         (inout)待旋转的二阶对称张量(s11, s12, s13, s22, s23, s33)
+ */
+void rot_zxy2zrt_symtensor2odr(double theta, double A[6]);
+
+
+/**
+ * 柱坐标下的位移偏导 ∂u(z,r,t)/∂(z,r,t) 转到 直角坐标 ∂u(z,x,y)/∂(z,x,y)
+ * 
+ *            uz       ur       ut                 uz       ux       uy
+ *  ∂z                                       ∂z
+ *  ∂r                                 --->  ∂x
+ *  1/r*∂t                                   ∂y
+ * 
+ * 
+ * @param   theta        (in)r轴相对x轴的旋转弧度
+ * @param   u[3]         (inout)柱坐标下的位移矢量
+ * @param   upar[3][3]   (inout)柱坐标下的位移空间偏导
+ * @param   r            (in)r轴坐标
+ */
+void rot_zrt2zxy_upar(const double theta, double u[3], double upar[3][3], const double r);
\ No newline at end of file
diff --git a/pygrt/C_extension/include/dynamic/dwm.h b/pygrt/C_extension/include/common/dwm.h
similarity index 94%
rename from pygrt/C_extension/include/dynamic/dwm.h
rename to pygrt/C_extension/include/common/dwm.h
index 813e79e8..d04519b2 100644
--- a/pygrt/C_extension/include/dynamic/dwm.h
+++ b/pygrt/C_extension/include/common/dwm.h
@@ -13,7 +13,10 @@
 
 #pragma once 
 
+#include <stdio.h>
+
 #include "common/model.h"
+#include "common/kernel.h"
 
 
 /**
@@ -44,6 +47,7 @@
  * @param  sum_DC_uir_J[nr][3][4]   (out)双力偶源
  * 
  * @param  fstats[nr])          (out)不同震中距的格林函数积分过程文件
+ * @param  kerfunc              (in)计算核函数的函数指针
  * 
  * @return  k        积分截至时的波数
  */
@@ -57,4 +61,4 @@ MYREAL discrete_integ(
     MYCOMPLEX sum_HF_uiz_J[nr][3][4],  MYCOMPLEX sum_DC_uiz_J[nr][3][4],  
     MYCOMPLEX sum_EXP_uir_J[nr][3][4], MYCOMPLEX sum_VF_uir_J[nr][3][4],  
     MYCOMPLEX sum_HF_uir_J[nr][3][4],  MYCOMPLEX sum_DC_uir_J[nr][3][4],  
-    FILE *(fstats[nr]));
+    FILE *(fstats[nr]), KernelFunc kerfunc);
diff --git a/pygrt/C_extension/include/dynamic/fim.h b/pygrt/C_extension/include/common/fim.h
similarity index 67%
rename from pygrt/C_extension/include/dynamic/fim.h
rename to pygrt/C_extension/include/common/fim.h
index 9be2ebfd..ba8954b2 100755
--- a/pygrt/C_extension/include/dynamic/fim.h
+++ b/pygrt/C_extension/include/common/fim.h
@@ -12,8 +12,11 @@
 
 #pragma once 
 
+#include <stdio.h>
+
 #include "common/const.h"
 #include "common/model.h"
+#include "common/kernel.h"
 
 
 
@@ -50,6 +53,7 @@
  * @param  sum_DC_uir_J[nr][3][4]   (out)双力偶源
  * 
  * @param  fstats[nr]           (out)不同震中距的格林函数积分过程文件
+ * @param  kerfunc              (in)计算核函数的函数指针
  * 
  * @return  k        积分截至时的波数
  */
@@ -63,34 +67,6 @@ MYREAL linear_filon_integ(
     MYCOMPLEX sum_HF_uiz_J[nr][3][4],  MYCOMPLEX sum_DC_uiz_J[nr][3][4],  
     MYCOMPLEX sum_EXP_uir_J[nr][3][4], MYCOMPLEX sum_VF_uir_J[nr][3][4],  
     MYCOMPLEX sum_HF_uir_J[nr][3][4],  MYCOMPLEX sum_DC_uir_J[nr][3][4],  
-    FILE *(fstats[nr]));
-
+    FILE *(fstats[nr]), KernelFunc kerfunc);
 
 
-/**
- *  和int_Pk函数类似，不过是计算核函数和渐近Bessel函数的乘积，其中涉及两种数组形状：
- *    + [3][3]. 存储的是核函数，第一个维度3代表阶数m=0,1,2，第二个维度3代表三类系数qm,wm,vm  
- *    + [3][4]. 存储的是该dk区间内的积分值，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
- * 
- * 
- * @param     k     (in)波数  
- * @param     r     (in)震中距 
- * @param    EXP_qwv[3][3]    (in)爆炸源核函数
- * @param    VF_qwv[3][3]     (in)垂直力源核函数
- * @param    HF_qwv[3][3]     (in)水平力源核函数
- * @param    DC_qwv[3][3]     (in)双力偶源核函数 
- * @param    calc_uir         (in)是否计算ui_r（位移u对坐标r的偏导）
- * @param    EXP_J[3][4]      (out)爆炸源，该dk区间内的积分值，下同
- * @param    VF_J[3][4]       (out)垂直力源
- * @param    HF_J[3][4]       (out)水平力源
- * @param    DC_J[3][4]       (out)双力偶源
- *  
- */
-void int_Pk_filon(
-    MYREAL k, MYREAL r, 
-    const MYCOMPLEX EXP_qwv[3][3], const MYCOMPLEX VF_qwv[3][3], 
-    const MYCOMPLEX HF_qwv[3][3],  const MYCOMPLEX DC_qwv[3][3], 
-    bool calc_uir, 
-    MYCOMPLEX EXP_J[3][4], MYCOMPLEX VF_J[3][4], 
-    MYCOMPLEX HF_J[3][4],  MYCOMPLEX DC_J[3][4] );
-
diff --git a/pygrt/C_extension/include/common/integral.h b/pygrt/C_extension/include/common/integral.h
new file mode 100644
index 00000000..81a0acae
--- /dev/null
+++ b/pygrt/C_extension/include/common/integral.h
@@ -0,0 +1,92 @@
+/**
+ * @file   integral.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-03
+ * 
+ *     将被积函数的逐点值累加成积分值
+ *                   
+ */
+
+#pragma once 
+
+#include "common/const.h"
+
+/**
+ * 计算核函数和Bessel函数的乘积，相当于计算了一个小积分区间内的值。参数中涉及两种数组形状：
+ *    + [3][3]. 存储的是核函数，第一个维度3代表阶数m=0,1,2，第二个维度3代表三类系数qm,wm,vm  
+ *    + [3][4]. 存储的是该dk区间内的积分值，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
+ * 
+ * 
+ * @param     k     (in)波数
+ * @param     r     (in)震中距 
+ * @param    EXP_qwv[3][3]    (in)爆炸源核函数
+ * @param    VF_qwv[3][3]     (in)垂直力源核函数
+ * @param    HF_qwv[3][3]     (in)水平力源核函数
+ * @param    DC_qwv[3][3]     (in)双力偶源核函数
+ * @param    calc_uir         (in)是否计算ui_r（位移u对坐标r的偏导）
+ * @param    EXP_J[3][4]      (out)爆炸源，该dk区间内的积分值，下同
+ * @param    VF_J[3][4]       (out)垂直力源
+ * @param    HF_J[3][4]       (out)水平力源
+ * @param    DC_J[3][4]       (out)双力偶源
+ * 
+ */
+void int_Pk(
+    MYREAL k, MYREAL r, 
+    const MYCOMPLEX EXP_qwv[3][3], const MYCOMPLEX VF_qwv[3][3], 
+    const MYCOMPLEX HF_qwv[3][3],  const MYCOMPLEX DC_qwv[3][3], 
+    bool calc_uir,
+    MYCOMPLEX EXP_J[3][4], MYCOMPLEX VF_J[3][4], 
+    MYCOMPLEX HF_J[3][4],  MYCOMPLEX DC_J[3][4] );
+
+
+
+
+/**
+ * 将最终计算好的多个积分值，按照公式(5.6.22)组装成3分量。数组形状[3][4]，\
+ * 存储的是最终的积分值，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
+ * 
+ * @param    sum_EXP_J[3][4]      (in)爆炸源，最终的积分值，下同
+ * @param    sum_VF_J[3][4]       (in)垂直力源
+ * @param    sum_HF_J[3][4]       (in)水平力源
+ * @param    sum_DC_J[3][4]       (in)双力偶源
+ * @param    tol_EXP[2]           (out)爆炸源的Z、R分量频谱结果
+ * @param    tol_VF[2]            (out)垂直力源的Z、R分量频谱结果
+ * @param    tol_HF[3]            (out)水平力源的Z、R、T分量频谱结果
+ * @param    tol_DD[2]            (out)45度倾滑的Z、R分量频谱结果
+ * @param    tol_DS[3]            (out)90度倾滑的Z、R、T分量频谱结果
+ * @param    tol_SS[3]            (out)90度走滑的Z、R、T分量频谱结果
+ */
+void merge_Pk(
+    const MYCOMPLEX sum_EXP_J[3][4], const MYCOMPLEX sum_VF_J[3][4], 
+    const MYCOMPLEX sum_HF_J[3][4],  const MYCOMPLEX sum_DC_J[3][4], 
+    MYCOMPLEX tol_EXP[2], MYCOMPLEX tol_VF[2], MYCOMPLEX tol_HF[3],
+    MYCOMPLEX tol_DD[2],  MYCOMPLEX tol_DS[3], MYCOMPLEX tol_SS[3]);
+
+
+
+/**
+ *  和int_Pk函数类似，不过是计算核函数和渐近Bessel函数的乘积，其中涉及两种数组形状：
+ *    + [3][3]. 存储的是核函数，第一个维度3代表阶数m=0,1,2，第二个维度3代表三类系数qm,wm,vm  
+ *    + [3][4]. 存储的是该dk区间内的积分值，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
+ * 
+ * 
+ * @param     k     (in)波数  
+ * @param     r     (in)震中距 
+ * @param    EXP_qwv[3][3]    (in)爆炸源核函数
+ * @param    VF_qwv[3][3]     (in)垂直力源核函数
+ * @param    HF_qwv[3][3]     (in)水平力源核函数
+ * @param    DC_qwv[3][3]     (in)双力偶源核函数 
+ * @param    calc_uir         (in)是否计算ui_r（位移u对坐标r的偏导）
+ * @param    EXP_J[3][4]      (out)爆炸源，该dk区间内的积分值，下同
+ * @param    VF_J[3][4]       (out)垂直力源
+ * @param    HF_J[3][4]       (out)水平力源
+ * @param    DC_J[3][4]       (out)双力偶源
+ *  
+ */
+void int_Pk_filon(
+    MYREAL k, MYREAL r, 
+    const MYCOMPLEX EXP_qwv[3][3], const MYCOMPLEX VF_qwv[3][3], 
+    const MYCOMPLEX HF_qwv[3][3],  const MYCOMPLEX DC_qwv[3][3], 
+    bool calc_uir, 
+    MYCOMPLEX EXP_J[3][4], MYCOMPLEX VF_J[3][4], 
+    MYCOMPLEX HF_J[3][4],  MYCOMPLEX DC_J[3][4] );
diff --git a/pygrt/C_extension/include/dynamic/iostats.h b/pygrt/C_extension/include/common/iostats.h
similarity index 100%
rename from pygrt/C_extension/include/dynamic/iostats.h
rename to pygrt/C_extension/include/common/iostats.h
diff --git a/pygrt/C_extension/include/common/kernel.h b/pygrt/C_extension/include/common/kernel.h
new file mode 100644
index 00000000..f9d2e86a
--- /dev/null
+++ b/pygrt/C_extension/include/common/kernel.h
@@ -0,0 +1,21 @@
+/**
+ * @file   kernel.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-06
+ * 
+ *    动态或静态下计算核函数的函数指针
+ * 
+ */
+
+
+#pragma once 
+
+
+#include "common/model.h"
+
+
+typedef void (*KernelFunc) (
+    const MODEL1D *mod1d, MYCOMPLEX omega, MYREAL k,
+    MYCOMPLEX EXP_qwv[3][3], MYCOMPLEX VF_qwv[3][3], MYCOMPLEX HF_qwv[3][3], MYCOMPLEX DC_qwv[3][3],
+    bool calc_uiz,
+    MYCOMPLEX EXP_uiz_qwv[3][3], MYCOMPLEX VF_uiz_qwv[3][3], MYCOMPLEX HF_uiz_qwv[3][3], MYCOMPLEX DC_uiz_qwv[3][3]);
\ No newline at end of file
diff --git a/pygrt/C_extension/include/common/model.h b/pygrt/C_extension/include/common/model.h
index c8da5f1f..1892fa8f 100755
--- a/pygrt/C_extension/include/common/model.h
+++ b/pygrt/C_extension/include/common/model.h
@@ -23,6 +23,8 @@ typedef struct _LAYER {
     MYREAL Qainv; ///<   1/Q_p
     MYREAL Qbinv; ///<   1/Q_s
     MYCOMPLEX mu;   ///< \f$ V_b^2 * \rho \f$
+    MYCOMPLEX lambda;   ///< \f$ V_a^2 * \rho - 2*\mu \f$
+    MYCOMPLEX delta;   ///< \f$ (\lambda+\mu)/(\lambda+3*\mu) \f$
     MYCOMPLEX kaka;  ///<  \f$ (\omega/V_a)^2 \f$
     MYCOMPLEX kbkb;  ///<  \f$ (\omega/V_b)^2 \f$
 } LAYER;
diff --git a/pygrt/C_extension/include/dynamic/ptam.h b/pygrt/C_extension/include/common/ptam.h
similarity index 95%
rename from pygrt/C_extension/include/dynamic/ptam.h
rename to pygrt/C_extension/include/common/ptam.h
index 94a8116d..f6c7ff38 100755
--- a/pygrt/C_extension/include/dynamic/ptam.h
+++ b/pygrt/C_extension/include/common/ptam.h
@@ -16,7 +16,10 @@
 
 #pragma once 
 
+#include <stdio.h>
+
 #include "common/model.h"
+#include "common/kernel.h"
 
 
 #define PTAM_MAX_PEAK_TROUGH 36  ///< 最后统计波峰波谷的目标数量
@@ -53,6 +56,7 @@
  * 
  * @param    fstats[nr]          (out)波数积分过程文件指针
  * @param    ptam_fstats[nr]     (out)峰谷平均法过程文件指针
+ * @param    kerfunc              (in)计算核函数的函数指针
  * 
  * 
  */
@@ -66,7 +70,7 @@ void PTA_method(
     MYCOMPLEX sum_HF_uiz_J0[nr][3][4],  MYCOMPLEX sum_DC_uiz_J0[nr][3][4],  
     MYCOMPLEX sum_EXP_uir_J0[nr][3][4], MYCOMPLEX sum_VF_uir_J0[nr][3][4],  
     MYCOMPLEX sum_HF_uir_J0[nr][3][4],  MYCOMPLEX sum_DC_uir_J0[nr][3][4], 
-    FILE *(fstats[nr]), FILE *(ptam_fstats[nr]));
+    FILE *(fstats[nr]), FILE *(ptam_fstats[nr]), KernelFunc kerfunc);
 
 
 
diff --git a/pygrt/C_extension/include/dynamic/quadratic.h b/pygrt/C_extension/include/common/quadratic.h
similarity index 100%
rename from pygrt/C_extension/include/dynamic/quadratic.h
rename to pygrt/C_extension/include/common/quadratic.h
diff --git a/pygrt/C_extension/include/common/radiation.h b/pygrt/C_extension/include/common/radiation.h
new file mode 100644
index 00000000..30d2f88a
--- /dev/null
+++ b/pygrt/C_extension/include/common/radiation.h
@@ -0,0 +1,36 @@
+/**
+ * @file   radiation.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-06
+ * 
+ *    计算不同震源的辐射因子
+ * 
+ */
+
+#pragma once
+
+
+#define GRT_SYN_COMPUTE_EX 0   ///< 计算爆炸源
+#define GRT_SYN_COMPUTE_SF 1   ///< 计算单力源
+#define GRT_SYN_COMPUTE_DC 2   ///< 计算双力偶源
+#define GRT_SYN_COMPUTE_MT 3   ///< 计算矩张量源
+
+
+/**
+ * 设置每个震源的方向因子
+ * 
+ * @param      srcCoef         (out)方向因子，[3]表示ZRT三分量，[6]表示6个震源(EX,VF,HF,DD,DS,SS)
+ * @param      computeType     (in)要计算的震源类型，使用宏定义
+ * @param      par_theta       (in)方向因子中是否对theta(az)求导
+ * @param      M0              (in)放大系数，对于位错源、爆炸源、张量震源，M0是标量地震矩；对于单力源，M0是放大系数
+ * @param      coef            (in)放大系数，用于位移空间导数的计算
+ * @param      azrad           (in)弧度制的方位角
+ * @param      mchn            (in)震源机制参数，
+ *                                 对于单力源，mchn={fn, fe, fz}，
+ *                                 对于位错源，mchn={strike, dip, rake}，
+ *                                 对于张量源，mchn={Mxx, Mxy, Mxz, Myy, Myz, Mzz}
+ */
+void set_source_radiation(
+    double srcCoef[3][6], const int computeType, const bool par_theta,
+    const double M0, const double coef, const double azrad, const double mchn[6]
+);
\ No newline at end of file
diff --git a/pygrt/C_extension/include/common/recursion.h b/pygrt/C_extension/include/common/recursion.h
new file mode 100644
index 00000000..73299bfa
--- /dev/null
+++ b/pygrt/C_extension/include/common/recursion.h
@@ -0,0 +1,251 @@
+/**
+ * @file   recursion.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-03
+ * 
+ * 以下代码通过递推公式计算两层的广义反射透射系数矩阵 ，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *
+ */
+
+
+#pragma once 
+
+
+#include "common/const.h"
+
+
+/**
+ * 根据公式(5.5.3(1))进行递推  
+ * 
+ * @param     RD1[2][2]       (in)1层 P-SV 下传反射系数矩阵
+ * @param     RDL1            (in)1层 SH 下传反射系数
+ * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
+ * @param     RUL1            (in)1层 SH 上传反射系数
+ * @param     TD1[2][2]       (in)1层 P-SV 下传透射系数矩阵
+ * @param     TDL1            (in)1层 SH 下传透射系数
+ * @param     TU1[2][2]       (in)1层 P-SV 上传透射系数矩阵
+ * @param     TUL1            (in)1层 SH 上传透射系数
+ * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
+ * @param     RDL2            (in)2层 SH 下传反射系数
+ * @param     RD[2][2]        (out)1+2层 P-SV 下传反射系数矩阵
+ * @param     RDL             (out)1+2层 SH 下传反射系数
+ * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_U^1 \mathbf{R}_D^2)^{-1} \mathbf{T}_D^1 \f$ 一项   
+ * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
+ * 
+ */
+void recursion_RD(
+    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
+    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
+
+
+/**
+ * 根据公式(5.5.3(2))进行递推 
+ * 
+ * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
+ * @param     RUL1            (in)1层 SH 上传反射系数
+ * @param     TD1[2][2]       (in)1层 P-SV 下传透射系数矩阵
+ * @param     TDL1            (in)1层 SH 下传透射系数
+ * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
+ * @param     RDL2            (in)2层 SH 下传反射系数
+ * @param     TD2[2][2]       (in)2层 P-SV 下传透射系数矩阵
+ * @param     TDL2            (in)2层 SH 下传透射系数
+ * @param     TD[2][2]        (out)1+2层 P-SV 下传透射系数矩阵
+ * @param     TDL             (out)1+2层 SH 下传透射系数
+ * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_U^1 \mathbf{R}_D^2)^{-1} \mathbf{T}_D^1 \f$ 一项   
+ * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
+ * 
+ */
+void recursion_TD(
+    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, 
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
+    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, 
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
+
+
+
+
+/**
+ * 根据公式(5.5.3(3))进行递推  
+ * 
+ * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
+ * @param     RUL1            (in)1层 SH 上传反射系数
+ * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
+ * @param     RDL2            (in)2层 SH 下传反射系数
+ * @param     RU2[2][2]       (in)2层 P-SV 上传反射系数矩阵
+ * @param     RUL2            (in)2层 SH 上传反射系数
+ * @param     TD2[2][2]       (in)2层 P-SV 下传透射系数矩阵
+ * @param     TDL2            (in)2层 SH 下传透射系数
+ * @param     TU2[2][2]       (in)2层 P-SV 上传透射系数矩阵
+ * @param     TUL2            (in)2层 SH 上传透射系数
+ * @param     RU[2][2]        (out)1+2层 P-SV 上传反射系数矩阵
+ * @param     RUL             (out)1+2层 SH 上传反射系数
+ * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_D^2 \mathbf{R}_U^1)^{-1} \mathbf{T}_U^2 \f$ 一项   
+ * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
+ * 
+ */
+void recursion_RU(
+    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
+    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
+    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
+
+/**
+ * 根据公式(5.5.3(4))进行递推
+ * 
+ * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
+ * @param     RUL1            (in)1层 SH 上传反射系数
+ * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
+ * @param     RDL2            (in)2层 SH 下传反射系数
+ * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
+ * @param     RDL2            (in)2层 SH 下传反射系数
+ * @param     TU2[2][2]       (in)2层 P-SV 上传透射系数矩阵
+ * @param     TUL2            (in)2层 SH 上传透射系数
+ * @param     TU[2][2]        (out)1+2层 P-SV 上传透射系数矩阵
+ * @param     TUL             (out)1+2层 SH 上传透射系数
+ * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_D^2 \mathbf{R}_U^1)^{-1} \mathbf{T}_U^2 \f$ 一项   
+ * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
+ * 
+ * 
+ */
+void recursion_TU(
+    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2,
+    const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
+    MYCOMPLEX TU[2][2], MYCOMPLEX *TUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
+
+
+
+/**
+ * 根据公式(5.5.3)进行递推，相当于上面四个函数合并
+ * 
+ * @param     RD1[2][2]       (in)1层 P-SV 下传反射系数矩阵
+ * @param     RDL1            (in)1层 SH 下传反射系数
+ * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
+ * @param     RUL1            (in)1层 SH 上传反射系数
+ * @param     TD1[2][2]       (in)1层 P-SV 下传透射系数矩阵
+ * @param     TDL1            (in)1层 SH 下传透射系数
+ * @param     TU1[2][2]       (in)1层 P-SV 上传透射系数矩阵
+ * @param     TUL1            (in)1层 SH 上传透射系数
+ * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
+ * @param     RDL2            (in)2层 SH 下传反射系数
+ * @param     RU2[2][2]       (in)2层 P-SV 上传反射系数矩阵
+ * @param     RUL2            (in)2层 SH 上传反射系数
+ * @param     TD2[2][2]       (in)2层 P-SV 下传透射系数矩阵
+ * @param     TDL2            (in)2层 SH 下传透射系数
+ * @param     TU2[2][2]       (in)2层 P-SV 上传透射系数矩阵
+ * @param     TUL2            (in)2层 SH 上传透射系数
+ * @param     RD[2][2]        (out)1+2层 P-SV 下传反射系数矩阵
+ * @param     RDL             (out)1+2层 SH 下传反射系数
+ * @param     RU[2][2]        (out)1+2层 P-SV 上传反射系数矩阵
+ * @param     RUL             (out)1+2层 SH 上传反射系数
+ * @param     TD[2][2]        (out)1+2层 P-SV 下传透射系数矩阵
+ * @param     TDL             (out)1+2层 SH 下传透射系数
+ * @param     TU[2][2]        (out)1+2层 P-SV 上传透射系数矩阵
+ * @param     TUL             (out)1+2层 SH 上传透射系数
+ * 
+ */
+void recursion_RT_2x2(
+    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
+    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
+    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX RU[2][2], MYCOMPLEX *RUL,
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL);
+
+
+/**
+ * 对于虚拟层位，即上下层是相同的物性参数，对公式(5.5.3)进行简化，只剩下时间延迟因子
+ * 
+ * @param     xa1      (in)P波归一化垂直波数 \f$ \sqrt{1 - (k_a/k)^2} \f$
+ * @param     xb1      (in)S波归一化垂直波数 \f$ \sqrt{1 - (k_b/k)^2} \f$
+ * @param     thk      (in)厚度
+ * @param     k         (in)波数
+ * @param     RU[2][2]       (inout)上层 P-SV 上传反射系数矩阵
+ * @param     RUL            (inout)上层 SH 上传反射系数
+ * @param     TD[2][2]       (inout)上层 P-SV 下传透射系数矩阵
+ * @param     TDL            (inout)上层 SH 下传透射系数
+ * @param     TU[2][2]       (inout)上层 P-SV 上传透射系数矩阵
+ * @param     TUL            (inout)上层 SH 上传透射系数
+ */
+void recursion_RT_2x2_imaginary(
+    MYCOMPLEX xa1, MYCOMPLEX xb1, MYREAL thk, MYREAL k, // 使用上层的厚度
+    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, 
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL);
+
+
+
+/**
+ * 最终公式(5.7.12,13,26,27)简化为 (P-SV波) :
+ * + 当台站在震源上方时：
+ * 
+ * \f[
+ * \begin{pmatrix}
+ * q_m \\
+ * w_m 
+ * \end{pmatrix}
+ * =
+ * \mathbf{R_1}
+ * 
+ * \left[
+ * \mathbf{R_2}
+ * \begin{pmatrix}
+ * P_m^+ \\
+ * SV_m^+ 
+ * \end{pmatrix}
+ * +
+ * \begin{pmatrix}
+ * P_m^- \\
+ * SV_m^- 
+ * \end{pmatrix}
+ * 
+ * \right]
+ * 
+ * \f]
+ * 
+ * + 当台站在震源下方时：
+ * 
+ * \f[
+ * \begin{pmatrix}
+ * q_m \\
+ * w_m 
+ * \end{pmatrix}
+ * =
+ * \mathbf{R_1}
+ * 
+ * \left[
+ * \begin{pmatrix}
+ * P_m^+ \\
+ * SV_m^+ 
+ * \end{pmatrix}
+ * +
+ * \mathbf{R_2}
+ * \begin{pmatrix}
+ * P_m^- \\
+ * SV_m^- 
+ * \end{pmatrix}
+ * 
+ * \right]
+ * 
+ * \f]
+ * 
+ * SH波类似，但是是标量形式。 
+ * 
+ * @param     ircvup    (in)接收层是否浅于震源层
+ * @param     R1[2][2]  (in)P-SV波，\f$\mathbf{R_1}\f$矩阵
+ * @param     RL1       (in)SH波，  \f$ R_1\f$
+ * @param     R2[2][2]  (in)P-SV波，\f$\mathbf{R_2}\f$矩阵
+ * @param     RL2       (in)SH波，  \f$ R_2\f$
+ * @param     coef[3][2]  (in)震源系数，维度3表示震源附近的\f$ q_m,w_m,v_m\f$  ，维度2表示下行波(p=0)和上行波(p=1)
+ * @param     qwv[3]      (out)最终通过矩阵传播计算出的在台站位置的\f$ q_m,w_m,v_m\f$
+ */
+void get_qwv(
+    bool ircvup, 
+    const MYCOMPLEX R1[2][2], MYCOMPLEX RL1, 
+    const MYCOMPLEX R2[2][2], MYCOMPLEX RL2, 
+    const MYCOMPLEX coef[3][2], MYCOMPLEX qwv[3]);
diff --git a/pygrt/C_extension/include/dynamic/layer.h b/pygrt/C_extension/include/dynamic/layer.h
index 5fbdaa26..71487315 100755
--- a/pygrt/C_extension/include/dynamic/layer.h
+++ b/pygrt/C_extension/include/dynamic/layer.h
@@ -50,6 +50,20 @@ void calc_R_EV(
     MYCOMPLEX R_EV[2][2], MYCOMPLEX *R_EVL);
 
 
+/**
+ * 计算接收点位置的ui_z的接收矩阵，即将波场转为ui_z。
+ * 公式本质是推导ui_z关于q_m, w_m, v_m的连接矩阵（就是应力推导过程的一部分）
+ * 
+ * @param     xa_rcv      (in)接受层的P波归一化垂直波数 \f$ \sqrt{1 - (k_a/k)^2} \f$
+ * @param     xb_rcv      (in)接受层的S波归一化垂直波数 \f$ \sqrt{1 - (k_b/k)^2} \f$
+ * @param     ircvup      (in)接收点是否浅于震源层
+ * @param     k           (in)波数
+ * @param     R[2][2]     (in)P-SV波场
+ * @param     RL          (in)SH波场
+ * @param     R_EV[2][2]  (out)P-SV接收函数矩阵
+ * @param     R_EVL       (out)SH接收函数值
+ * 
+ */
 void calc_uiz_R_EV(
     MYCOMPLEX xa_rcv, MYCOMPLEX xb_rcv, bool ircvup,
     MYREAL k, 
diff --git a/pygrt/C_extension/include/dynamic/propagate.h b/pygrt/C_extension/include/dynamic/propagate.h
index 4002a77b..c98c3624 100755
--- a/pygrt/C_extension/include/dynamic/propagate.h
+++ b/pygrt/C_extension/include/dynamic/propagate.h
@@ -18,8 +18,6 @@
 #include "common/model.h"
 
 
-
-
 /**
  * kernel函数根据(5.5.3)式递推计算广义反射透射矩阵， 再根据公式得到
  * 
@@ -103,286 +101,3 @@ void kernel(
 
 
 
-/**
- * 计算核函数和Bessel函数的乘积，相当于计算了一个小积分区间内的值。参数中涉及两种数组形状：
- *    + [3][3]. 存储的是核函数，第一个维度3代表阶数m=0,1,2，第二个维度3代表三类系数qm,wm,vm  
- *    + [3][4]. 存储的是该dk区间内的积分值，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
- * 
- * 
- * @param     k     (in)波数
- * @param     r     (in)震中距 
- * @param    EXP_qwv[3][3]    (in)爆炸源核函数
- * @param    VF_qwv[3][3]     (in)垂直力源核函数
- * @param    HF_qwv[3][3]     (in)水平力源核函数
- * @param    DC_qwv[3][3]     (in)双力偶源核函数
- * @param    calc_uir         (in)是否计算ui_r（位移u对坐标r的偏导）
- * @param    EXP_J[3][4]      (out)爆炸源，该dk区间内的积分值，下同
- * @param    VF_J[3][4]       (out)垂直力源
- * @param    HF_J[3][4]       (out)水平力源
- * @param    DC_J[3][4]       (out)双力偶源
- * 
- */
-void int_Pk(
-    MYREAL k, MYREAL r, 
-    const MYCOMPLEX EXP_qwv[3][3], const MYCOMPLEX VF_qwv[3][3], 
-    const MYCOMPLEX HF_qwv[3][3],  const MYCOMPLEX DC_qwv[3][3], 
-    bool calc_uir,
-    MYCOMPLEX EXP_J[3][4], MYCOMPLEX VF_J[3][4], 
-    MYCOMPLEX HF_J[3][4],  MYCOMPLEX DC_J[3][4] );
-
-
-/**
- * 将最终计算好的多个积分值，按照公式(5.6.22)组装成3分量。数组形状[3][4]，\
- * 存储的是最终的积分值，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
- * 
- * @param    sum_EXP_J[3][4]      (in)爆炸源，最终的积分值，下同
- * @param    sum_VF_J[3][4]       (in)垂直力源
- * @param    sum_HF_J[3][4]       (in)水平力源
- * @param    sum_DC_J[3][4]       (in)双力偶源
- * @param    tol_EXP[2]           (out)爆炸源的Z、R分量频谱结果
- * @param    tol_VF[2]            (out)垂直力源的Z、R分量频谱结果
- * @param    tol_HF[3]            (out)水平力源的Z、R、T分量频谱结果
- * @param    tol_DD[2]            (out)45度倾滑的Z、R分量频谱结果
- * @param    tol_DS[3]            (out)90度倾滑的Z、R、T分量频谱结果
- * @param    tol_SS[3]            (out)90度走滑的Z、R、T分量频谱结果
- */
-void merge_Pk(
-    const MYCOMPLEX sum_EXP_J[3][4], const MYCOMPLEX sum_VF_J[3][4], 
-    const MYCOMPLEX sum_HF_J[3][4],  const MYCOMPLEX sum_DC_J[3][4], 
-    MYCOMPLEX tol_EXP[2], MYCOMPLEX tol_VF[2], MYCOMPLEX tol_HF[3],
-    MYCOMPLEX tol_DD[2],  MYCOMPLEX tol_DS[3], MYCOMPLEX tol_SS[3]);
-
-
-/**
- * 根据公式(5.5.3(1))进行递推  
- * 
- * @param     RD1[2][2]       (in)1层 P-SV 下传反射系数矩阵
- * @param     RDL1            (in)1层 SH 下传反射系数
- * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
- * @param     RUL1            (in)1层 SH 上传反射系数
- * @param     TD1[2][2]       (in)1层 P-SV 下传透射系数矩阵
- * @param     TDL1            (in)1层 SH 下传透射系数
- * @param     TU1[2][2]       (in)1层 P-SV 上传透射系数矩阵
- * @param     TUL1            (in)1层 SH 上传透射系数
- * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
- * @param     RDL2            (in)2层 SH 下传反射系数
- * @param     RD[2][2]        (out)1+2层 P-SV 下传反射系数矩阵
- * @param     RDL             (out)1+2层 SH 下传反射系数
- * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_U^1 \mathbf{R}_D^2)^{-1} \mathbf{T}_D^1 \f$ 一项   
- * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
- * 
- */
-void recursion_RD(
-    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
-    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
-
-
-/**
- * 根据公式(5.5.3(2))进行递推 
- * 
- * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
- * @param     RUL1            (in)1层 SH 上传反射系数
- * @param     TD1[2][2]       (in)1层 P-SV 下传透射系数矩阵
- * @param     TDL1            (in)1层 SH 下传透射系数
- * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
- * @param     RDL2            (in)2层 SH 下传反射系数
- * @param     TD2[2][2]       (in)2层 P-SV 下传透射系数矩阵
- * @param     TDL2            (in)2层 SH 下传透射系数
- * @param     TD[2][2]        (out)1+2层 P-SV 下传透射系数矩阵
- * @param     TDL             (out)1+2层 SH 下传透射系数
- * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_U^1 \mathbf{R}_D^2)^{-1} \mathbf{T}_D^1 \f$ 一项   
- * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
- * 
- */
-void recursion_TD(
-    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, 
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
-    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, 
-    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
-
-
-
-
-/**
- * 根据公式(5.5.3(3))进行递推  
- * 
- * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
- * @param     RUL1            (in)1层 SH 上传反射系数
- * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
- * @param     RDL2            (in)2层 SH 下传反射系数
- * @param     RU2[2][2]       (in)2层 P-SV 上传反射系数矩阵
- * @param     RUL2            (in)2层 SH 上传反射系数
- * @param     TD2[2][2]       (in)2层 P-SV 下传透射系数矩阵
- * @param     TDL2            (in)2层 SH 下传透射系数
- * @param     TU2[2][2]       (in)2层 P-SV 上传透射系数矩阵
- * @param     TUL2            (in)2层 SH 上传透射系数
- * @param     RU[2][2]        (out)1+2层 P-SV 上传反射系数矩阵
- * @param     RUL             (out)1+2层 SH 上传反射系数
- * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_D^2 \mathbf{R}_U^1)^{-1} \mathbf{T}_U^2 \f$ 一项   
- * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
- * 
- */
-void recursion_RU(
-    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
-    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
-    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
-
-/**
- * 根据公式(5.5.3(4))进行递推
- * 
- * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
- * @param     RUL1            (in)1层 SH 上传反射系数
- * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
- * @param     RDL2            (in)2层 SH 下传反射系数
- * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
- * @param     RDL2            (in)2层 SH 下传反射系数
- * @param     TU2[2][2]       (in)2层 P-SV 上传透射系数矩阵
- * @param     TUL2            (in)2层 SH 上传透射系数
- * @param     TU[2][2]        (out)1+2层 P-SV 上传透射系数矩阵
- * @param     TUL             (out)1+2层 SH 上传透射系数
- * @param     inv_2x2T[2][2]  (out) 非NULL时，返回公式中的 \f$ (\mathbf{I} - \mathbf{R}_D^2 \mathbf{R}_U^1)^{-1} \mathbf{T}_U^2 \f$ 一项   
- * @param     invT            (out) 非NULL时，返回上面inv_2x2T的标量形式      
- * 
- * 
- */
-void recursion_TU(
-    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2,
-    const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
-    MYCOMPLEX TU[2][2], MYCOMPLEX *TUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT);
-
-
-
-/**
- * 根据公式(5.5.3)进行递推，相当于上面四个函数合并
- * 
- * @param     RD1[2][2]       (in)1层 P-SV 下传反射系数矩阵
- * @param     RDL1            (in)1层 SH 下传反射系数
- * @param     RU1[2][2]       (in)1层 P-SV 上传反射系数矩阵
- * @param     RUL1            (in)1层 SH 上传反射系数
- * @param     TD1[2][2]       (in)1层 P-SV 下传透射系数矩阵
- * @param     TDL1            (in)1层 SH 下传透射系数
- * @param     TU1[2][2]       (in)1层 P-SV 上传透射系数矩阵
- * @param     TUL1            (in)1层 SH 上传透射系数
- * @param     RD2[2][2]       (in)2层 P-SV 下传反射系数矩阵
- * @param     RDL2            (in)2层 SH 下传反射系数
- * @param     RU2[2][2]       (in)2层 P-SV 上传反射系数矩阵
- * @param     RUL2            (in)2层 SH 上传反射系数
- * @param     TD2[2][2]       (in)2层 P-SV 下传透射系数矩阵
- * @param     TDL2            (in)2层 SH 下传透射系数
- * @param     TU2[2][2]       (in)2层 P-SV 上传透射系数矩阵
- * @param     TUL2            (in)2层 SH 上传透射系数
- * @param     RD[2][2]        (out)1+2层 P-SV 下传反射系数矩阵
- * @param     RDL             (out)1+2层 SH 下传反射系数
- * @param     RU[2][2]        (out)1+2层 P-SV 上传反射系数矩阵
- * @param     RUL             (out)1+2层 SH 上传反射系数
- * @param     TD[2][2]        (out)1+2层 P-SV 下传透射系数矩阵
- * @param     TDL             (out)1+2层 SH 下传透射系数
- * @param     TU[2][2]        (out)1+2层 P-SV 上传透射系数矩阵
- * @param     TUL             (out)1+2层 SH 上传透射系数
- * 
- */
-void recursion_RT_2x2(
-    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
-    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
-    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX RU[2][2], MYCOMPLEX *RUL,
-    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL);
-
-
-/**
- * 对于虚拟层位，即上下层是相同的物性参数，对公式(5.5.3)进行简化，只剩下时间延迟因子
- * 
- * @param     xa1      (in)P波归一化垂直波数 \f$ \sqrt{1 - (k_a/k)^2} \f$
- * @param     xb1      (in)S波归一化垂直波数 \f$ \sqrt{1 - (k_b/k)^2} \f$
- * @param     thk      (in)厚度
- * @param     k         (in)波数
- * @param     RU[2][2]       (inout)上层 P-SV 上传反射系数矩阵
- * @param     RUL            (inout)上层 SH 上传反射系数
- * @param     TD[2][2]       (inout)上层 P-SV 下传透射系数矩阵
- * @param     TDL            (inout)上层 SH 下传透射系数
- * @param     TU[2][2]       (inout)上层 P-SV 上传透射系数矩阵
- * @param     TUL            (inout)上层 SH 上传透射系数
- */
-void recursion_RT_2x2_imaginary(
-    MYCOMPLEX xa1, MYCOMPLEX xb1, MYREAL thk, MYREAL k, // 使用上层的厚度
-    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, 
-    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL);
-
-
-
-/**
- * 最终公式(5.7.12,13,26,27)简化为 (P-SV波) :
- * + 当台站在震源上方时：
- * 
- * \f[
- * \begin{pmatrix}
- * q_m \\
- * w_m 
- * \end{pmatrix}
- * =
- * \mathbf{R_1}
- * 
- * \left[
- * \mathbf{R_2}
- * \begin{pmatrix}
- * P_m^+ \\
- * SV_m^+ 
- * \end{pmatrix}
- * +
- * \begin{pmatrix}
- * P_m^- \\
- * SV_m^- 
- * \end{pmatrix}
- * 
- * \right]
- * 
- * \f]
- * 
- * + 当台站在震源下方时：
- * 
- * \f[
- * \begin{pmatrix}
- * q_m \\
- * w_m 
- * \end{pmatrix}
- * =
- * \mathbf{R_1}
- * 
- * \left[
- * \begin{pmatrix}
- * P_m^+ \\
- * SV_m^+ 
- * \end{pmatrix}
- * +
- * \mathbf{R_2}
- * \begin{pmatrix}
- * P_m^- \\
- * SV_m^- 
- * \end{pmatrix}
- * 
- * \right]
- * 
- * \f]
- * 
- * SH波类似，但是是标量形式。 
- * 
- * @param     ircvup    (in)接收层是否浅于震源层
- * @param     R1[2][2]  (in)P-SV波，\f$\mathbf{R_1}\f$矩阵
- * @param     RL1       (in)SH波，  \f$ R_1\f$
- * @param     R2[2][2]  (in)P-SV波，\f$\mathbf{R_2}\f$矩阵
- * @param     RL2       (in)SH波，  \f$ R_2\f$
- * @param     coef[3][2]  (in)震源系数，维度3表示震源附近的\f$ q_m,w_m,v_m\f$  ，维度2表示下行波(p=0)和上行波(p=1)
- * @param     qwv[3]      (out)最终通过矩阵传播计算出的在台站位置的\f$ q_m,w_m,v_m\f$
- */
-void get_qwv(
-    bool ircvup, 
-    const MYCOMPLEX R1[2][2], MYCOMPLEX RL1, 
-    const MYCOMPLEX R2[2][2], MYCOMPLEX RL2, 
-    const MYCOMPLEX coef[3][2], MYCOMPLEX qwv[3]);
diff --git a/pygrt/C_extension/include/static/static_layer.h b/pygrt/C_extension/include/static/static_layer.h
new file mode 100644
index 00000000..4397c5ec
--- /dev/null
+++ b/pygrt/C_extension/include/static/static_layer.h
@@ -0,0 +1,91 @@
+/**
+ * @file   static_layer.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ * 以下代码实现的是 静态反射透射系数矩阵 ，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *         2. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *              透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ *
+ */
+
+#pragma once
+
+#include <stdbool.h>
+
+#include "common/const.h"
+
+/**
+ * 计算自由表面的静态反射系数，公式(6.3.12)
+ * 
+ * @param     delta1           (in)表层的 \f$ \Delta \f$
+ * @param     R_tilt[2][2]     (out)P-SV系数矩阵，SH系数为1
+ * 
+ */
+void calc_static_R_tilt(MYCOMPLEX delta1, MYCOMPLEX R_tilt[2][2]);
+
+
+/**
+ * 计算接收点位置的静态接收矩阵，将波场转为位移，公式(6.3.35,37)
+ * 
+ * @param     ircvup      (in)接收点是否浅于震源层
+ * @param     k           (in)波数
+ * @param     R[2][2]     (in)P-SV波场
+ * @param     RL          (in)SH波场
+ * @param     R_EV[2][2]  (out)P-SV接收函数矩阵
+ * @param     R_EVL       (out)SH接收函数值
+ * 
+ */
+void calc_static_R_EV(
+    bool ircvup,
+    const MYCOMPLEX R[2][2], MYCOMPLEX RL, 
+    MYCOMPLEX R_EV[2][2], MYCOMPLEX *R_EVL);
+
+
+/**
+ * 计算接收点位置的ui_z的静态接收矩阵，即将波场转为ui_z。
+ * 公式本质是推导ui_z关于q_m, w_m, v_m的连接矩阵（就是应力推导过程的一部分）
+ * 
+ * @param     delta1      (in)接收层的 \f$ \Delta \f$
+ * @param     ircvup      (in)接收点是否浅于震源层
+ * @param     k           (in)波数
+ * @param     R[2][2]     (in)P-SV波场
+ * @param     RL          (in)SH波场
+ * @param     R_EV[2][2]  (out)P-SV接收函数矩阵
+ * @param     R_EVL       (out)SH接收函数值
+ * 
+ */
+void calc_static_uiz_R_EV(
+    MYCOMPLEX delta1, bool ircvup, MYREAL k, 
+    const MYCOMPLEX R[2][2], MYCOMPLEX RL, 
+    MYCOMPLEX R_EV[2][2], MYCOMPLEX *R_EVL);
+
+
+/**
+ * 计算界面的静态反射系数RD/RDL/RU/RUL, 静态透射系数TD/TDL/TU/TUL, 包括时间延迟因子，
+ * 后缀L表示SH波的系数, 其余表示P-SV波的系数, 根据公式(6.3.18)  
+ * 
+ * @param      delta1    (in)上层的 \f$ \Delta \f$
+ * @param      mu1       (in)上层的剪切模量
+ * @param      delta2    (in)下层的 \f$ \Delta \f$
+ * @param      mu2       (in)下层的剪切模量
+ * @param      thk       (in)上层层厚
+ * @param      k         (in)波数
+ * @param      RD[2][2]  (out)P-SV 下传反射系数矩阵
+ * @param      RDL       (out)SH 下传反射系数
+ * @param      RU[2][2]  (out)P-SV 上传反射系数矩阵
+ * @param      RUL       (out)SH 上传反射系数
+ * @param      TD[2][2]  (out)P-SV 下传透射系数矩阵
+ * @param      TDL       (out)SH 下传透射系数
+ * @param      TU[2][2]  (out)P-SV 上传透射系数矩阵
+ * @param      TUL       (out)SH 上传透射系数
+ * 
+ */
+void calc_static_RT_2x2(
+    MYCOMPLEX delta1, MYCOMPLEX mu1, 
+    MYCOMPLEX delta2, MYCOMPLEX mu2, 
+    MYREAL thk, MYREAL k,
+    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, 
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL);    
\ No newline at end of file
diff --git a/pygrt/C_extension/include/static/static_propagate.h b/pygrt/C_extension/include/static/static_propagate.h
new file mode 100644
index 00000000..f539a11e
--- /dev/null
+++ b/pygrt/C_extension/include/static/static_propagate.h
@@ -0,0 +1,30 @@
+/**
+ * @file   static_propagate.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ * 以下代码实现的是 静态广义反射透射系数矩阵 ，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *         2. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *              透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ *
+ */
+
+#pragma once 
+
+#include "common/const.h"
+#include "common/model.h"
+
+
+/**
+ * 静态kernel函数根据(5.5.3)式递推计算静态广义反射透射矩阵。递推公式适用于动态和静态情况。
+ * 函数参数与动态kernel函数保持一致，具体说明详见`dynamic/propagate.h`。
+ * 
+ * 此处omega未使用，传入0即可
+ */
+void static_kernel(
+    const MODEL1D *mod1d, MYCOMPLEX omega, MYREAL k,
+    MYCOMPLEX EXP_qwv[3][3], MYCOMPLEX VF_qwv[3][3], MYCOMPLEX HF_qwv[3][3], MYCOMPLEX DC_qwv[3][3],
+    bool calc_uiz,
+    MYCOMPLEX EXP_uiz_qwv[3][3], MYCOMPLEX VF_uiz_qwv[3][3], MYCOMPLEX HF_uiz_qwv[3][3], MYCOMPLEX DC_uiz_qwv[3][3]);
\ No newline at end of file
diff --git a/pygrt/C_extension/include/static/static_source.h b/pygrt/C_extension/include/static/static_source.h
new file mode 100644
index 00000000..6aa98ef3
--- /dev/null
+++ b/pygrt/C_extension/include/static/static_source.h
@@ -0,0 +1,31 @@
+/**
+ * @file   static_source.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ * 以下代码实现的是 静态震源系数————剪切源， 参考：
+ *             1. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *                透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ *
+ */
+#pragma once
+
+#include "common/const.h"
+ 
+/**
+ * 计算不同震源的静态震源系数，文献/书中仅提供双力偶源的震源系数，其它震源系数重新推导
+ * 
+ * 数组形状[3][3][2]，代表在[i][j][p]时表示m=i阶时的
+ * P(j=0),SV(j=1),SH(j=2)的震源系数(分别可记为q,w,v)，且分为下行波(p=0)和上行波(p=1). 
+ * 
+ * @param  delta           (in)震源层的\f$ \Delta \f$
+ * @param  k               (in)波数
+ * @param  EXP[3][3][2]    (out)爆炸源的震源系数，下同
+ * @param  VF[3][3][2]     (out)垂直力源
+ * @param  HF[3][3][2]     (out)水平力源
+ * @param  DC[3][3][2]     (out)双力偶源
+ */
+void static_source_coef(
+    MYCOMPLEX delta, MYREAL k,
+    MYCOMPLEX EXP[3][3][2], MYCOMPLEX VF[3][3][2], MYCOMPLEX HF[3][3][2], MYCOMPLEX DC[3][3][2]);
+ 
\ No newline at end of file
diff --git a/pygrt/C_extension/include/static/stgrt.h b/pygrt/C_extension/include/static/stgrt.h
new file mode 100644
index 00000000..e998b38d
--- /dev/null
+++ b/pygrt/C_extension/include/static/stgrt.h
@@ -0,0 +1,79 @@
+/**
+ * @file   stgrt.h
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-03
+ * 
+ * 以下代码实现的是 广义反射透射系数矩阵+离散波数法 计算静态格林函数，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *         2. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *              透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ * 
+ */
+
+
+
+#pragma once
+
+
+#include "common/model.h"
+
+
+/**
+ * 积分计算Z, R, T三个分量静态格林函数的核心函数
+ * 
+ * @param      pymod1d      (in)`PYMODEL1D` 结构体指针 
+ * @param      nr           (in)震中距数量
+ * @param      rs           (in)震中距数组 
+ * @param      keps         (in)波数积分的收敛条件，要求在某震中距下所有格林函数都收敛，为负数代表不提前判断收敛，按照波数积分上限进行积分 
+ * @param      k0           (in)波数积分的上限
+ * @param      Length       (in)波数k积分间隔 \f$ dk=2\pi/(fabs(L)*r_{max}) \f$ , 如果为负数，则使用线性Filon积分
+ * @param      EXPgrn[nr][2]      (out)浮点数数组，爆炸源的Z、R分量频谱结果
+ * @param      VFgrn[nr][2]       (out)浮点数数组，垂直力源的Z、R分量频谱结果
+ * @param      HFgrn[nr][3]       (out)浮点数数组，水平力源的Z、R、T分量频谱结果
+ * @param      DDgrn[nr][2]       (out)浮点数数组，45度倾滑的Z、R分量频谱结果
+ * @param      DSgrn[nr][3]       (out)浮点数数组，90度倾滑的Z、R、T分量频谱结果
+ * @param      SSgrn[nr][3]       (out)浮点数数组，90度走滑的Z、R、T分量频谱结果
+ *
+ * @param      calc_upar              (in)是否计算位移u的空间导数
+ * @param      EXPgrn_uiz[nr][2]      (out)浮点数数组，爆炸源产生的ui_z(位移u对坐标z的偏导)的Z、R分量频谱结果，下同
+ * @param      VFgrn_uiz[nr][2]       (out)浮点数数组，垂直力源的Z、R分量频谱结果
+ * @param      HFgrn_uiz[nr][3]       (out)浮点数数组，水平力源的Z、R、T分量频谱结果
+ * @param      DDgrn_uiz[nr][2]       (out)浮点数数组，45度倾滑的Z、R分量频谱结果
+ * @param      DSgrn_uiz[nr][3]       (out)浮点数数组，90度倾滑的Z、R、T分量频谱结果
+ * @param      SSgrn_uiz[nr][3]       (out)浮点数数组，90度走滑的Z、R、T分量频谱结果
+ * @param      EXPgrn_uir[nr][2]      (out)浮点数数组，爆炸源产生的ui_r(位移u对坐标r的偏导)的Z、R分量频谱结果，下同
+ * @param      VFgrn_uir[nr][2]       (out)浮点数数组，垂直力源的Z、R分量频谱结果
+ * @param      HFgrn_uir[nr][3]       (out)浮点数数组，水平力源的Z、R、T分量频谱结果
+ * @param      DDgrn_uir[nr][2]       (out)浮点数数组，45度倾滑的Z、R分量频谱结果
+ * @param      DSgrn_uir[nr][3]       (out)浮点数数组，90度倾滑的Z、R、T分量频谱结果
+ * @param      SSgrn_uir[nr][3]       (out)浮点数数组，90度走滑的Z、R、T分量频谱结果
+ * 
+ */
+void integ_static_grn(
+    PYMODEL1D *pymod1d, MYINT nr, MYREAL *rs, MYREAL vmin_ref, MYREAL keps, MYREAL k0, MYREAL Length,
+
+    // 返回值，维度2代表Z、R分量，维度3代表Z、R、T分量
+    MYREAL EXPgrn[nr][2], // EXZ, EXR 的实部和虚部
+    MYREAL VFgrn[nr][2],  // VFZ, VFR 的实部和虚部
+    MYREAL HFgrn[nr][3],  // HFZ, HFR, HFT 的实部和虚部
+    MYREAL DDgrn[nr][2],  // DDZ, DDR 的实部和虚部      [DD: 45-dip slip]
+    MYREAL DSgrn[nr][3],  // DSZ, DSR, DST 的实部和虚部 [DS: 90-dip slip]
+    MYREAL SSgrn[nr][3],  // SSZ, SSR, SST 的实部和虚部 [SS: strike slip]
+
+    bool calc_upar,
+    MYREAL EXPgrn_uiz[nr][2], // EXZ, EXR 的实部和虚部
+    MYREAL VFgrn_uiz[nr][2],  // VFZ, VFR 的实部和虚部
+    MYREAL HFgrn_uiz[nr][3],  // HFZ, HFR, HFT 的实部和虚部
+    MYREAL DDgrn_uiz[nr][2],  // DDZ, DDR 的实部和虚部      [DD: 45-dip slip]
+    MYREAL DSgrn_uiz[nr][3],  // DSZ, DSR, DST 的实部和虚部 [DS: 90-dip slip]
+    MYREAL SSgrn_uiz[nr][3],  // SSZ, SSR, SST 的实部和虚部 [SS: strike slip]
+    MYREAL EXPgrn_uir[nr][2], // EXZ, EXR 的实部和虚部
+    MYREAL VFgrn_uir[nr][2],  // VFZ, VFR 的实部和虚部
+    MYREAL HFgrn_uir[nr][3],  // HFZ, HFR, HFT 的实部和虚部
+    MYREAL DDgrn_uir[nr][2],  // DDZ, DDR 的实部和虚部      [DD: 45-dip slip]
+    MYREAL DSgrn_uir[nr][3],  // DSZ, DSR, DST 的实部和虚部 [DS: 90-dip slip]
+    MYREAL SSgrn_uir[nr][3],  // SSZ, SSR, SST 的实部和虚部 [SS: strike slip]
+
+    const char *statsstr // 积分结果输出
+);
\ No newline at end of file
diff --git a/pygrt/C_extension/src/common/Makefile b/pygrt/C_extension/src/common/Makefile
index 76c3ec9e..08e65f11 100644
--- a/pygrt/C_extension/src/common/Makefile
+++ b/pygrt/C_extension/src/common/Makefile
@@ -7,6 +7,9 @@ SRCS := $(wildcard *.c)
 OBJS := $(patsubst %.c, $(BUILD_DIR)/%.o, $(SRCS))
 DEPS := $(OBJS:.o=.d)
 
+progs: 
+	@echo "No rule to make target 'progs'"
+
 all: objs
 
 objs: $(BUILD_DIR) $(OBJS)
diff --git a/pygrt/C_extension/src/common/coord.c b/pygrt/C_extension/src/common/coord.c
new file mode 100644
index 00000000..37e4e700
--- /dev/null
+++ b/pygrt/C_extension/src/common/coord.c
@@ -0,0 +1,102 @@
+/**
+ * @file   coord.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-10
+ * 
+ * 关于坐标变换的一些函数
+ * 
+ */
+
+#include <stdbool.h>
+#include <math.h>
+
+
+
+void rot_zxy2zrt_vec(double theta, double A[3]){
+    double s1, s2, s3;
+    s1 = A[0];  s2 = A[1];  s3 = A[2];
+    double st = sin(theta);
+    double ct = cos(theta);
+    A[0] = s1;
+    A[1] = s2*ct + s3*st;
+    A[2] = -s2*st + s3*ct;
+}
+
+
+
+void rot_zxy2zrt_symtensor2odr(double theta, double A[6]) {
+    double s11, s12, s13, s22, s23, s33;
+    s11 = A[0];   s12 = A[1];   s13 = A[2];
+                  s22 = A[3];   s23 = A[4];
+                                s33 = A[5];
+    double st = sin(theta);
+    double ct = cos(theta);
+    double sst = st*st;
+    double cct = ct*ct;
+    double sct = st*ct;
+    A[0] = s11;
+    A[1] = s12*ct + s13*st;
+    A[2] = -s12*st + s13*ct;
+    A[3] = s22*cct + s33*sst + 2.0*s23*sct;
+    A[4] = (s33 - s22)*sct + s23*(cct - sst);
+    A[5] = s22*sst + s33*cct - 2.0*s23*sct;
+    
+}
+
+
+
+void rot_zrt2zxy_upar(const double theta, double u[3], double upar[3][3], const double r){
+    double s00, s01, s02;
+    double s10, s11, s12;
+    double s20, s21, s22;
+    //           uz       ur       ut
+    //  ∂z
+    //  ∂r
+    //  1/r*∂t
+    s00 = upar[0][0]; s01 = upar[0][1]; s02 = upar[0][2];
+    s10 = upar[1][0]; s11 = upar[1][1]; s12 = upar[1][2];
+    s20 = upar[2][0]; s21 = upar[2][1]; s22 = upar[2][2];
+
+    double u0, u1, u2;
+    u0 = u[0];  u1 = u[1];  u2 = u[2];
+
+    double st = sin(theta);
+    double ct = cos(theta);
+    double sst = st*st;
+    double cct = ct*ct;
+    double sct = st*ct;
+
+    //           uz       ux       uy
+    //  ∂z
+    //  ∂x
+    //  ∂y
+
+    // ∂ uz / ∂ z
+    upar[0][0] = s00;
+    // ∂ ux / ∂ z
+    upar[0][1] = s01*ct - s02*st;
+    // ∂ uy / ∂ z
+    upar[0][2] = s01*st + s02*ct;
+
+
+    // ∂ uz / ∂ x
+    upar[1][0] = s10*ct - s20*st;
+    // ∂ ux / ∂ x
+    upar[1][1] = s11*cct + s22*sst - (s12+s21)*sct + u1*sst/r + u2*sct/r;
+    // ∂ uy / ∂ x
+    upar[1][2] = s12*cct - s21*sst + (s11-s22)*sct - u1*sct/r + u2*sst/r;
+
+
+    // ∂ uz / ∂ y
+    upar[2][0] = s10*st + s20*ct;
+    // ∂ ux / ∂ y
+    upar[2][1] = s21*cct - s12*sst + (s11-s22)*sct - u1*sct/r - u2*cct/r;
+    // ∂ uy / ∂ y
+    upar[2][2] = s22*cct + s11*sst + (s12+s21)*sct + u1*cct/r - u2*sct/r;
+
+
+    // 转矢量
+    u[0] = u0;
+    u[1] = u1*ct - u2*st;
+    u[2] = u1*st + u2*ct;
+}
\ No newline at end of file
diff --git a/pygrt/C_extension/src/dynamic/dwm.c b/pygrt/C_extension/src/common/dwm.c
similarity index 95%
rename from pygrt/C_extension/src/dynamic/dwm.c
rename to pygrt/C_extension/src/common/dwm.c
index 18819dab..68eb13bb 100644
--- a/pygrt/C_extension/src/dynamic/dwm.c
+++ b/pygrt/C_extension/src/common/dwm.c
@@ -15,9 +15,10 @@
 #include <stdio.h> 
 #include <stdlib.h>
 
-#include "dynamic/dwm.h"
-#include "dynamic/propagate.h"
-#include "dynamic/iostats.h"
+#include "common/dwm.h"
+#include "common/kernel.h"
+#include "common/integral.h"
+#include "common/iostats.h"
 #include "common/model.h"
 #include "common/const.h"
 
@@ -32,7 +33,7 @@ MYREAL discrete_integ(
     MYCOMPLEX sum_HF_uiz_J[nr][3][4],  MYCOMPLEX sum_DC_uiz_J[nr][3][4],  
     MYCOMPLEX sum_EXP_uir_J[nr][3][4], MYCOMPLEX sum_VF_uir_J[nr][3][4],  
     MYCOMPLEX sum_HF_uir_J[nr][3][4],  MYCOMPLEX sum_DC_uir_J[nr][3][4],  
-    FILE *(fstats[nr]))
+    FILE *(fstats[nr]), KernelFunc kerfunc)
 {
     MYCOMPLEX EXP_J[3][4], VF_J[3][4], HF_J[3][4],  DC_J[3][4];
 
@@ -72,8 +73,8 @@ MYREAL discrete_integ(
 
         // printf("w=%15.5e, ik=%d\n", CREAL(omega), ik);
         // 计算核函数 F(k, w)
-        kernel(mod1d, omega, k, pEXP_qwv, pVF_qwv, pHF_qwv, pDC_qwv, 
-            calc_upar, pEXP_uiz_qwv, pVF_uiz_qwv, pHF_uiz_qwv, pDC_uiz_qwv); 
+        kerfunc(mod1d, omega, k, pEXP_qwv, pVF_qwv, pHF_qwv, pDC_qwv, 
+                calc_upar, pEXP_uiz_qwv, pVF_uiz_qwv, pHF_uiz_qwv, pDC_uiz_qwv); 
 
 
         // 震中距rs循环
diff --git a/pygrt/C_extension/src/dynamic/fim.c b/pygrt/C_extension/src/common/fim.c
similarity index 97%
rename from pygrt/C_extension/src/dynamic/fim.c
rename to pygrt/C_extension/src/common/fim.c
index ceac3d4d..18eca4dd 100755
--- a/pygrt/C_extension/src/dynamic/fim.c
+++ b/pygrt/C_extension/src/common/fim.c
@@ -14,17 +14,14 @@
 #include <complex.h>
 #include <stdlib.h>
 
-#include "dynamic/fim.h"
-#include "dynamic/iostats.h"
-#include "dynamic/propagate.h"
+#include "common/fim.h"
+#include "common/integral.h"
+#include "common/iostats.h"
 #include "common/const.h"
 #include "common/model.h"
 
 
 
-
-
-
 MYREAL linear_filon_integ(
     const MODEL1D *mod1d, MYREAL dk, MYREAL kmax, MYREAL keps, MYCOMPLEX omega, 
     MYINT nr, MYREAL *rs,
@@ -35,7 +32,7 @@ MYREAL linear_filon_integ(
     MYCOMPLEX sum_HF_uiz_J[nr][3][4],  MYCOMPLEX sum_DC_uiz_J[nr][3][4],  
     MYCOMPLEX sum_EXP_uir_J[nr][3][4], MYCOMPLEX sum_VF_uir_J[nr][3][4],  
     MYCOMPLEX sum_HF_uir_J[nr][3][4],  MYCOMPLEX sum_DC_uir_J[nr][3][4],  
-    FILE *(fstats[nr]))
+    FILE *(fstats[nr]), KernelFunc kerfunc)
 {   
     for(MYINT ir=0; ir<nr; ++ir){
         for(MYINT m=0; m<3; ++m){
@@ -110,8 +107,8 @@ MYREAL linear_filon_integ(
         if(k > kmax) break;
 
         // 计算核函数 F(k, w)
-        kernel(mod1d, omega, k, pEXP_qwv, pVF_qwv, pHF_qwv, pDC_qwv,
-               calc_upar, pEXP_uiz_qwv, pVF_uiz_qwv, pHF_uiz_qwv, pDC_uiz_qwv); 
+        kerfunc(mod1d, omega, k, pEXP_qwv, pVF_qwv, pHF_qwv, pDC_qwv,
+                calc_upar, pEXP_uiz_qwv, pVF_uiz_qwv, pHF_uiz_qwv, pDC_uiz_qwv); 
 
         // 震中距rs循环
         iendk = true;
diff --git a/pygrt/C_extension/src/common/integral.c b/pygrt/C_extension/src/common/integral.c
new file mode 100644
index 00000000..66020aa0
--- /dev/null
+++ b/pygrt/C_extension/src/common/integral.c
@@ -0,0 +1,138 @@
+/**
+ * @file   integral.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-03
+ * 
+ *     将被积函数的逐点值累加成积分值
+ *                   
+ */
+
+
+#include <stdio.h>
+#include <stdbool.h>
+
+#include "common/integral.h"
+#include "common/const.h"
+#include "common/bessel.h"
+
+
+
+void int_Pk(
+    MYREAL k, MYREAL r, 
+    // F(ki,w)， 第一个维度3代表阶数m=0,1,2，第二个维度3代表三类系数qm,wm,vm 
+    const MYCOMPLEX EXP_qwv[3][3], const MYCOMPLEX VF_qwv[3][3], 
+    const MYCOMPLEX HF_qwv[3][3],  const MYCOMPLEX DC_qwv[3][3], 
+    // F(ki,w)Jm(ki*r)ki，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
+    bool calc_uir,
+    MYCOMPLEX EXP_J[3][4], MYCOMPLEX VF_J[3][4], 
+    MYCOMPLEX HF_J[3][4],  MYCOMPLEX DC_J[3][4])
+{
+    MYREAL bj0k, bj1k, bj2k;
+    MYREAL kr = k*r;
+    MYREAL kr_inv = RONE/kr;
+    MYREAL kcoef = k;
+
+    MYREAL J1coef, J2coef;
+
+    bessel012(kr, &bj0k, &bj1k, &bj2k); 
+    if(calc_uir){
+        MYREAL j1, j2;
+        j1 = bj1k;
+        j2 = bj2k;
+        besselp012(kr, &bj0k, &bj1k, &bj2k); 
+        kcoef = k*k;
+
+        J1coef = kr_inv * (-kr_inv * j1 + bj1k);
+        J2coef = kr_inv * (-kr_inv * j2 + bj2k);
+    } else {
+        J1coef = bj1k*kr_inv;
+        J2coef = bj2k*kr_inv;
+    }
+
+    J1coef *= kcoef;
+    J2coef *= kcoef;
+
+    bj0k *= kcoef;
+    bj1k *= kcoef;
+    bj2k *= kcoef;
+
+    
+    if(EXP_qwv!=NULL){
+    // 公式(5.6.22), 将公式分解为F(k,w)Jm(kr)k的形式
+    // m=0 爆炸源
+    EXP_J[0][0] = - EXP_qwv[0][0]*bj1k;
+    EXP_J[0][2] =   EXP_qwv[0][1]*bj0k;
+    }
+
+    if(VF_qwv!=NULL){
+    // m=0 垂直力源
+    VF_J[0][0] = - VF_qwv[0][0]*bj1k;
+    VF_J[0][2] =   VF_qwv[0][1]*bj0k;
+    }
+
+    if(HF_qwv!=NULL){
+    // m=1 水平力源
+    HF_J[1][0]  =   HF_qwv[1][0]*bj0k;         // q1*J0*k
+    HF_J[1][1]  = - (HF_qwv[1][0] + HF_qwv[1][2])*J1coef;    // - (q1+v1)*J1*k/kr
+    HF_J[1][2]  =   HF_qwv[1][1]*bj1k;         // w1*J1*k
+    HF_J[1][3]  = - HF_qwv[1][2]*bj0k;         // -v1*J0*k
+    }
+
+    if(DC_qwv!=NULL){
+    // m=0 双力偶源
+    DC_J[0][0] = - DC_qwv[0][0]*bj1k;
+    DC_J[0][2] =   DC_qwv[0][1]*bj0k;
+
+    // m=1 双力偶源
+    DC_J[1][0]  =   DC_qwv[1][0]*bj0k;         // q1*J0*k
+    DC_J[1][1]  = - (DC_qwv[1][0] + DC_qwv[1][2])*J1coef;    // - (q1+v1)*J1*k/kr
+    DC_J[1][2]  =   DC_qwv[1][1]*bj1k;         // w1*J1*k
+    DC_J[1][3]  = - DC_qwv[1][2]*bj0k;         // -v1*J0*k
+
+    // m=2 双力偶源
+    DC_J[2][0]  =   DC_qwv[2][0]*bj1k;         // q2*J1*k
+    DC_J[2][1]  = - RTWO*(DC_qwv[2][0] + DC_qwv[2][2])*J2coef;    // - (q2+v2)*J2*k/kr
+    DC_J[2][2]  =   DC_qwv[2][1]*bj2k;         // w2*J2*k
+    DC_J[2][3]  = - DC_qwv[2][2]*bj1k;         // -v2*J1*k
+    }
+}
+
+
+
+void merge_Pk(
+    // F(ki,w)Jm(ki*r)ki，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
+    const MYCOMPLEX sum_EXP_J[3][4], const MYCOMPLEX sum_VF_J[3][4], 
+    const MYCOMPLEX sum_HF_J[3][4],  const MYCOMPLEX sum_DC_J[3][4], 
+    // 累积求和，维度2代表Z、R分量，维度3代表Z、R、T分量 
+    MYCOMPLEX tol_EXP[2], MYCOMPLEX tol_VF[2], MYCOMPLEX tol_HF[3],
+    MYCOMPLEX tol_DD[2],  MYCOMPLEX tol_DS[3], MYCOMPLEX tol_SS[3])
+{   
+    if(sum_EXP_J!=NULL){
+    tol_EXP[0] = sum_EXP_J[0][2];
+    tol_EXP[1] = sum_EXP_J[0][0];
+    }
+
+    if(sum_VF_J!=NULL){
+    tol_VF[0] = sum_VF_J[0][2];
+    tol_VF[1] = sum_VF_J[0][0];
+    }
+
+    if(sum_HF_J!=NULL){
+    tol_HF[0] = sum_HF_J[1][2];
+    tol_HF[1] = sum_HF_J[1][0] + sum_HF_J[1][1];
+    tol_HF[2] = - sum_HF_J[1][1] + sum_HF_J[1][3];
+    }
+
+    if(sum_DC_J!=NULL){
+    tol_DD[0] = sum_DC_J[0][2];
+    tol_DD[1] = sum_DC_J[0][0];
+    
+    tol_DS[0] = sum_DC_J[1][2];
+    tol_DS[1] = sum_DC_J[1][0] + sum_DC_J[1][1];
+    tol_DS[2] = - sum_DC_J[1][1] + sum_DC_J[1][3];
+
+    tol_SS[0] = sum_DC_J[2][2];
+    tol_SS[1] = sum_DC_J[2][0] + sum_DC_J[2][1];
+    tol_SS[2] = - sum_DC_J[2][1] + sum_DC_J[2][3];
+    }
+}
diff --git a/pygrt/C_extension/src/dynamic/iostats.c b/pygrt/C_extension/src/common/iostats.c
similarity index 98%
rename from pygrt/C_extension/src/dynamic/iostats.c
rename to pygrt/C_extension/src/common/iostats.c
index 409f4544..6be633ac 100755
--- a/pygrt/C_extension/src/dynamic/iostats.c
+++ b/pygrt/C_extension/src/common/iostats.c
@@ -11,7 +11,7 @@
 #include <stdbool.h>
 #include <complex.h>
 
-#include "dynamic/iostats.h"
+#include "common/iostats.h"
 #include "common/const.h"
 
 
diff --git a/pygrt/C_extension/src/common/model.c b/pygrt/C_extension/src/common/model.c
index 3836d2b7..9d59f0ce 100755
--- a/pygrt/C_extension/src/common/model.c
+++ b/pygrt/C_extension/src/common/model.c
@@ -25,6 +25,8 @@ void print_mod1d(const MODEL1D *mod1d){
         printf("     Va=%6.2f, Vb=%6.2f, thk=%6.2f, Rho=%6.2f, 1/Qa=%6.2e, 1/Qb=%6.2e\n",
                      lay->Va, lay->Vb, lay->thk, lay->Rho, lay->Qainv, lay->Qbinv);
         printf("     mu=(%e %+e I)\n", CREAL(lay->mu), CIMAG(lay->mu));
+        printf("     lambda=(%e %+e I)\n", CREAL(lay->lambda), CIMAG(lay->lambda));
+        printf("     delta=(%e %+e I)\n", CREAL(lay->delta), CIMAG(lay->delta));
         printf("     ka^2=%e%+eJ\n", CREAL(lay->kaka), CIMAG(lay->kaka));
         printf("     kb^2=%e%+eJ\n", CREAL(lay->kbkb), CIMAG(lay->kbkb));
         for(MYINT u=0; u<50; ++u){printf("---"); } printf("\n");
@@ -146,6 +148,8 @@ void get_mod1d(const PYMODEL1D *pymod1d, MODEL1D *mod1d){
         lay->Qbinv  = RONE/pymod1d->Qb[i];
 
         lay->mu = (lay->Vb)*(lay->Vb)*(lay->Rho);
+        lay->lambda = (lay->Va)*(lay->Va)*(lay->Rho) - RTWO*lay->mu;
+        lay->delta = (lay->lambda + lay->mu) / (lay->lambda + RTHREE*lay->mu);
     }
 
 }
@@ -176,6 +180,9 @@ void copy_mod1d(const MODEL1D *mod1d1, MODEL1D *mod1d2){
         lay2->mu = lay1->mu;
         lay2->kaka = lay1->kaka;
         lay2->kbkb = lay1->kbkb;
+
+        lay2->lambda = lay1->lambda;
+        lay2->delta = lay1->delta;
     }
     
 }
@@ -207,6 +214,8 @@ void update_mod1d_omega(MODEL1D *mod1d, MYCOMPLEX omega){
         lay->kbkb = kb0*kb0;
         
         lay->mu = (Vb0*atnb)*(Vb0*atnb)*(lay->Rho);
+        lay->lambda = (Va0*atnb)*(Va0*atnb)*(lay->Rho) - 2*lay->mu;
+        lay->delta = (lay->lambda + lay->mu) / (lay->lambda + 3*lay->mu);
     }
 
 #if Print_GRTCOEF == 1
diff --git a/pygrt/C_extension/src/dynamic/ptam.c b/pygrt/C_extension/src/common/ptam.c
similarity index 95%
rename from pygrt/C_extension/src/dynamic/ptam.c
rename to pygrt/C_extension/src/common/ptam.c
index 8e8e8436..475e642c 100755
--- a/pygrt/C_extension/src/dynamic/ptam.c
+++ b/pygrt/C_extension/src/common/ptam.c
@@ -18,10 +18,10 @@
 #include <complex.h>
 #include <stdlib.h>
 
-#include "dynamic/ptam.h"
-#include "dynamic/iostats.h"
-#include "dynamic/propagate.h"
-#include "dynamic/quadratic.h"
+#include "common/ptam.h"
+#include "common/quadratic.h"
+#include "common/integral.h"
+#include "common/iostats.h"
 #include "common/const.h"
 #include "common/model.h"
 
@@ -188,17 +188,14 @@ void PTA_method(
     MYCOMPLEX sum_HF_uiz_J0[nr][3][4],  MYCOMPLEX sum_DC_uiz_J0[nr][3][4],  
     MYCOMPLEX sum_EXP_uir_J0[nr][3][4], MYCOMPLEX sum_VF_uir_J0[nr][3][4],  
     MYCOMPLEX sum_HF_uir_J0[nr][3][4],  MYCOMPLEX sum_DC_uir_J0[nr][3][4],  
-    FILE *(fstats[nr]), FILE *(ptam_fstats[nr]))
+    FILE *(fstats[nr]), FILE *(ptam_fstats[nr]), KernelFunc kerfunc)
 {   
     // 需要兼容对正常收敛而不具有规律波峰波谷的序列
     // 有时序列收敛比较好，不表现为规律的波峰波谷，
     // 此时设置最大等待次数，超过直接设置为中间值
 
-    MYINT ik=0;
     const MYINT maxnwait = 9;     // 最大等待次数，不能太小
-    const MYREAL dk=PI/((maxnwait-1)*rmax); 
     MYREAL k=0.0;
-    const MYREAL precoef = dk/predk; // 提前乘dk系数，以抵消格林函数主函数计算时最后乘dk
 
     MYCOMPLEX EXP_qwv[3][3], VF_qwv[3][3], HF_qwv[3][3], DC_qwv[3][3]; // 不同震源的核函数
     MYCOMPLEX (*pEXP_qwv)[3] = (sum_EXP_J0!=NULL)? EXP_qwv : NULL;
@@ -215,13 +212,6 @@ void PTA_method(
 
     static const MYINT maxNpt=PTAM_MAX_PEAK_TROUGH; // 波峰波谷的目标
 
-    // 根据波峰波谷的目标也给出一个kmax，+5以防万一 
-    const MYREAL kmax = k0 + (maxNpt+5)*PI/rmin;
-
-    // 每个震中距是否已找齐慢收敛序列
-    bool iendk = true, iendk0 = false;
-    bool *iendkrs = (bool *)calloc(nr, sizeof(bool));
-    for(MYINT ir=0; ir<nr; ++ir) iendkrs[ir] = false;
 
     // 用于接收F(ki,w)Jm(ki*r)ki
     // 存储采样的值，维度3表示通过连续3个点来判断波峰或波谷
@@ -351,22 +341,26 @@ void PTA_method(
 
             }
         }
-        iendkrs[ir] = false;
     }
 
 
-    k = k0 - dk;
-    while(true){
-        k += dk;
-        if(k > kmax) break;
+    // 对于PTAM，不同震中距使用不同dk
+    for(MYINT ir=0; ir<nr; ++ir){
+        MYREAL dk = PI/((maxnwait-1)*rs[ir]); 
+        MYREAL precoef = dk/predk; // 提前乘dk系数，以抵消格林函数主函数计算时最后乘dk
+        // 根据波峰波谷的目标也给出一个kmax，+5以防万一 
+        MYREAL kmax = k0 + (maxNpt+5)*PI/rs[ir];
+
+        bool iendk0=false;
 
-        // 计算核函数 F(k, w)
-        kernel(mod1d, omega, k, pEXP_qwv, pVF_qwv, pHF_qwv, pDC_qwv,
-               calc_upar, pEXP_uiz_qwv, pVF_uiz_qwv, pHF_uiz_qwv, pDC_uiz_qwv); 
+        k = k0 - dk;
+        while(true){
+            k += dk;
+            if(k > kmax) break;
 
-        iendk=true;
-        for(MYINT ir=0; ir<nr; ++ir){
-            if(iendkrs[ir]) continue;        // 该震中距下的慢收敛序列已找齐
+            // 计算核函数 F(k, w)
+            kerfunc(mod1d, omega, k, pEXP_qwv, pVF_qwv, pHF_qwv, pDC_qwv,
+                    calc_upar, pEXP_uiz_qwv, pVF_uiz_qwv, pHF_uiz_qwv, pDC_uiz_qwv); 
 
             // 计算被积函数一项 F(k,w)Jm(kr)k
             int_Pk(k, rs[ir],
@@ -428,19 +422,11 @@ void PTA_method(
                     &iendk0);
             
             } // END if calc_upar
-            
 
-            iendkrs[ir] = iendk0;
-            iendk = iendk && iendkrs[ir];
 
-        } // end rs loop
-
-        ++ik;
-
-        // 所有震中距的慢收敛序列都已找到
-        if(iendk) break;
-
-    } // end k loop
+            if(iendk0) break;
+        }// end k loop
+    }
 
     // printf("w=%f, ik=%d\n", CREAL(omega), ik);
 
@@ -479,7 +465,6 @@ void PTA_method(
     }
 
 
-    free(iendkrs);
     free(EXP_J3); free(VF_J3); free(HF_J3); free(DC_J3);
     free(EXP_uiz_J3); free(VF_uiz_J3); free(HF_uiz_J3); free(DC_uiz_J3);
     free(EXP_uir_J3); free(VF_uir_J3); free(HF_uir_J3); free(DC_uir_J3);
diff --git a/pygrt/C_extension/src/dynamic/quadratic.c b/pygrt/C_extension/src/common/quadratic.c
similarity index 97%
rename from pygrt/C_extension/src/dynamic/quadratic.c
rename to pygrt/C_extension/src/common/quadratic.c
index ec99b6a9..fb2d756b 100755
--- a/pygrt/C_extension/src/dynamic/quadratic.c
+++ b/pygrt/C_extension/src/common/quadratic.c
@@ -12,7 +12,7 @@
 
 #include <stdio.h>
 
-#include "dynamic/quadratic.h"
+#include "common/quadratic.h"
 #include "common/const.h"
 
 
diff --git a/pygrt/C_extension/src/common/radiation.c b/pygrt/C_extension/src/common/radiation.c
new file mode 100644
index 00000000..a24fddc7
--- /dev/null
+++ b/pygrt/C_extension/src/common/radiation.c
@@ -0,0 +1,108 @@
+/**
+ * @file   radiation.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-06
+ * 
+ *    计算不同震源的辐射因子
+ * 
+ */
+
+#include <stdbool.h>
+
+#include "common/radiation.h"
+#include "common/const.h"
+
+void set_source_radiation(
+    double srcCoef[3][6], const int computeType, const bool par_theta,
+    const double M0, const double coef, const double azrad, const double mchn[6]
+){
+    double mult;
+    if(computeType == GRT_SYN_COMPUTE_SF){
+        mult = 1e-15*M0*coef;
+    } else {
+        mult = 1e-20*M0*coef;
+    }
+
+    double saz, caz;
+    saz = sin(azrad);
+    caz = cos(azrad);
+
+    if(computeType == GRT_SYN_COMPUTE_EX){
+        srcCoef[0][0] = srcCoef[1][0] = (par_theta)? 0.0 : mult; // Z/R
+        srcCoef[2][0] = 0.0; // T
+    }  
+    else if(computeType == GRT_SYN_COMPUTE_SF){
+        double A0, A1, A4;
+        double fn, fe, fz;
+        fn=mchn[0];   fe=mchn[1];  fz=mchn[2];   
+        A0 = fz*mult;
+        A1 = (fn*caz + fe*saz)*mult;
+        A4 = (- fn*saz + fe*caz)*mult;
+
+        // 公式(4.6.20)
+        srcCoef[0][1] = srcCoef[1][1] = (par_theta)? 0.0 : A0; // VF, Z/R
+        srcCoef[0][2] = srcCoef[1][2] = (par_theta)? A4 : A1; // HF, Z/R
+        srcCoef[2][1] = 0.0; // VF, T
+        srcCoef[2][2] = (par_theta)? -A1 : A4; // HF, T
+    }
+    else if(computeType == GRT_SYN_COMPUTE_DC){
+        double strike, dip, rake;
+        strike=mchn[0];   dip=mchn[1];   rake=mchn[2];
+        // 公式(4.8.35)
+        double stkrad = strike*DEG1;
+        double diprad = dip*DEG1;
+        double rakrad = rake*DEG1;
+        double therad = azrad - stkrad;
+        double srak, crak, sdip, cdip, sdip2, cdip2, sthe, cthe, sthe2, cthe2;
+        srak = sin(rakrad);     crak = cos(rakrad);
+        sdip = sin(diprad);     cdip = cos(diprad);
+        sdip2 = 2.0*sdip*cdip;  cdip2 = 2.0*cdip*cdip - 1.0;
+        sthe = sin(therad);     cthe = cos(therad);
+        sthe2 = 2.0*sthe*cthe;  cthe2 = 2.0*cthe*cthe - 1.0;
+
+        double A0, A1, A2, A4, A5;
+        A0 = mult * (0.5*sdip2*srak);
+        A1 = mult * (cdip*crak*cthe - cdip2*srak*sthe);
+        A2 = mult * (0.5*sdip2*srak*cthe2 + sdip*crak*sthe2);
+        A4 = mult * (- cdip2*srak*cthe - cdip*crak*sthe);
+        A5 = mult * (sdip*crak*cthe2 - 0.5*sdip2*srak*sthe2);
+
+        srcCoef[0][3] = srcCoef[1][3] = (par_theta)? 0.0 : A0; // DD, Z/R
+        srcCoef[0][4] = srcCoef[1][4] = (par_theta)? A4 : A1; // DS, Z/R
+        srcCoef[0][5] = srcCoef[1][5] = (par_theta)? 2.0*A5 : A2; // SS, Z/R
+        srcCoef[2][3] = 0.0; // DD, T
+        srcCoef[2][4] = (par_theta)? -A1 : A4;  // DS, T
+        srcCoef[2][5] = (par_theta)? -2.0*A2 : A5;  // DS, T
+    }
+    else if(computeType == GRT_SYN_COMPUTE_MT){
+        // 公式(4.9.7)但修改了各向同性的量
+        double M11, M12, M13, M22, M23, M33;
+        M11 = mchn[0];   M12 = mchn[1];   M13 = mchn[2];
+                         M22 = mchn[3];   M23 = mchn[4];
+                                          M33 = mchn[5];
+        double Mexp = (M11 + M22 + M33)/3.0;
+        M11 -= Mexp;
+        M22 -= Mexp;
+        M33 -= Mexp;
+
+        double saz2, caz2;
+        saz2 = 2.0*saz*caz;
+        caz2 = 2.0*caz*caz - 1.0;
+
+        double A0, A1, A2, A4, A5;
+        A0 = mult * ((2.0*M33 - M11 - M22)/6.0 );
+        A1 = mult * (- (M13*caz + M23*saz));
+        A2 = mult * (0.5*(M11 - M22)*caz2+ M12*saz2);
+        A4 = mult * (M13*saz - M23*caz);
+        A5 = mult * (-0.5*(M11 - M22)*saz2 + M12*caz2);
+
+        srcCoef[0][0] = srcCoef[1][0] = (par_theta)? 0.0 : mult*Mexp; // EX, Z/R
+        srcCoef[0][3] = srcCoef[1][3] = (par_theta)? 0.0 : A0; // DD, Z/R
+        srcCoef[0][4] = srcCoef[1][4] = (par_theta)? A4 : A1; // DS, Z/R
+        srcCoef[0][5] = srcCoef[1][5] = (par_theta)? 2.0*A5 : A2; // SS, Z/R
+        srcCoef[2][0] = 0.0; // EX, T
+        srcCoef[2][3] = 0.0; // DD, T
+        srcCoef[2][4] = (par_theta)? -A1 : A4;  // DS, T
+        srcCoef[2][5] = (par_theta)? -2.0*A2 : A5;  // DS, T
+    }
+}
\ No newline at end of file
diff --git a/pygrt/C_extension/src/common/recursion.c b/pygrt/C_extension/src/common/recursion.c
new file mode 100644
index 00000000..104a7add
--- /dev/null
+++ b/pygrt/C_extension/src/common/recursion.c
@@ -0,0 +1,266 @@
+/**
+ * @file   recursion.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-03
+ * 
+ * 以下代码通过递推公式计算两层的广义反射透射系数矩阵 ，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *
+ */
+
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <stdbool.h>
+
+#include "common/recursion.h"
+#include "common/const.h"
+#include "common/matrix.h"
+
+
+
+void recursion_RD(
+    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
+    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
+{
+    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
+
+    // RD, RDL
+    cmat2x2_mul(RU1, RD2, tmp1);
+    cmat2x2_one_sub(tmp1);
+    cmat2x2_inv(tmp1, tmp1);
+    cmat2x2_mul(tmp1, TD1, tmp2);
+    if(inv_2x2T!=NULL) cmat2x2_assign(tmp2, inv_2x2T);
+
+    cmat2x2_mul(RD2, tmp2, tmp1);
+    cmat2x2_mul(TU1, tmp1, tmp2);
+    cmat2x2_add(RD1, tmp2, RD);
+    inv1 = RONE / (RONE - RUL1*RDL2) * TDL1;
+    *RDL = RDL1 + TUL1*RDL2*inv1;
+    if(invT!=NULL)  *invT = inv1;
+}
+
+
+void recursion_TD(
+    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, 
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
+    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, 
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
+{
+    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
+
+    // TD, TDL
+    cmat2x2_mul(RU1, RD2, tmp2);
+    cmat2x2_one_sub(tmp2);
+    cmat2x2_inv(tmp2, tmp1);
+    cmat2x2_mul(tmp1, TD1, tmp2);
+    if(inv_2x2T!=NULL)  cmat2x2_assign(tmp2, inv_2x2T);
+    cmat2x2_mul(TD2, tmp2, TD);
+    
+    inv1 = RONE / (RONE - RUL1*RDL2) * TDL1;
+    *TDL = TDL2 * inv1;
+
+    if(invT!=NULL) *invT = inv1;
+}
+
+
+void recursion_RU(
+    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
+    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
+    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
+{
+    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
+
+    // RU, RUL
+    cmat2x2_mul(RD2, RU1, tmp2);
+    cmat2x2_one_sub(tmp2);
+    cmat2x2_inv(tmp2, tmp1);
+    cmat2x2_mul(tmp1, TU2, tmp2);
+    if(inv_2x2T!=NULL)  cmat2x2_assign(tmp2, inv_2x2T);
+
+    cmat2x2_mul(RU1, tmp2, tmp1); 
+    cmat2x2_mul(TD2, tmp1, tmp2);
+    cmat2x2_add(RU2, tmp2, RU);
+    inv1 = RONE / (RONE - RUL1*RDL2) * TUL2;
+    *RUL = RUL2 + TDL2*RUL1*inv1; 
+
+    if(invT!=NULL)  *invT = inv1;
+}
+
+
+void recursion_TU(
+    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2,
+    const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
+    MYCOMPLEX TU[2][2], MYCOMPLEX *TUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
+{
+    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
+
+    // TU, TUL
+    cmat2x2_mul(RD2, RU1, tmp2);
+    cmat2x2_one_sub(tmp2);
+    cmat2x2_inv(tmp2, tmp1);
+    cmat2x2_mul(tmp1, TU2, tmp2);
+    if(inv_2x2T!=NULL) cmat2x2_assign(tmp2, inv_2x2T);
+    cmat2x2_mul(TU1, tmp2, TU);
+    
+    inv1 = RONE / (RONE - RUL1*RDL2) * TUL2;
+    *TUL = TUL1 * inv1;
+
+    if(invT!=NULL)  *invT = inv1;
+
+}
+
+
+
+
+void recursion_RT_2x2(
+    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
+    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
+    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
+    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
+    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX RU[2][2], MYCOMPLEX *RUL,
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL)
+{
+
+    // 临时矩阵
+    MYCOMPLEX tmp1[2][2], tmp2[2][2];
+    MYCOMPLEX inv0, inv1T;
+
+    inv0 = RONE / (RONE - RUL1*RDL2);
+    // return;
+
+    // Rayleigh RD,TD
+    if( RD!=NULL || TD!=NULL ){
+        cmat2x2_mul(RU1, RD2, tmp1);
+        cmat2x2_one_sub(tmp1);
+        cmat2x2_inv(tmp1, tmp1);
+        cmat2x2_mul(tmp1, TD1, tmp2);
+
+        // TD
+        if(TD!=NULL){
+            cmat2x2_mul(TD2, tmp2, TD); // 相同的逆阵，节省计算量
+        }
+
+        // RD
+        if(RD!=NULL){
+            cmat2x2_mul(RD2, tmp2, tmp1);
+            cmat2x2_mul(TU1, tmp1, tmp2);
+            cmat2x2_add(RD1, tmp2, RD);
+        }
+    }
+
+    // Rayleigh RU,TU
+    if( RU!=NULL || TU!=NULL ){
+        cmat2x2_mul(RD2, RU1, tmp1);
+        cmat2x2_one_sub(tmp1);
+        cmat2x2_inv(tmp1, tmp1);
+        cmat2x2_mul(tmp1, TU2, tmp2);
+
+        // TU
+        if(TU!=NULL){
+            cmat2x2_mul(TU1, tmp2, TU);
+        }
+
+        // RU
+        if(RU!=NULL){
+            cmat2x2_mul(RU1, tmp2, tmp1);
+            cmat2x2_mul(TD2, tmp1, tmp2);
+            cmat2x2_add(RU2, tmp2, RU);
+        }
+    }
+
+
+    // Love RDL,TDL
+    if(RDL!=NULL || TDL!=NULL){
+        inv1T = inv0 * TDL1;
+        // TDL
+        if(TDL!=NULL){
+            *TDL = TDL2 * inv1T;
+        }
+        // RDL
+        if(RDL!=NULL){
+            *RDL = RDL1 + TUL1*RDL2*inv1T;
+        }
+    }
+
+    // Love RUL,TUL
+    if(RUL!=NULL || TUL!=NULL){
+        inv1T = inv0 * TUL2;
+        // TUL
+        if(TUL!=NULL){
+            *TUL = TUL1 * inv1T;
+        }
+
+        // RUL
+        if(RUL!=NULL){
+            *RUL = RUL2 + TDL2*RUL1 *inv1T; 
+        }
+    }
+
+}
+
+
+void recursion_RT_2x2_imaginary(
+    MYCOMPLEX xa1, MYCOMPLEX xb1, MYREAL thk, MYREAL k, // 使用上层的厚度
+    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, 
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL)
+{
+    MYCOMPLEX exa, exb, exab, ex2a, ex2b; 
+    exa = CEXP(-k*thk*xa1);
+    exb = CEXP(-k*thk*xb1);
+
+    exab = exa * exb;
+    ex2a = exa * exa;
+    ex2b = exb * exb;
+
+
+    // 虚拟层位不是介质物理间断面
+    RU[0][0] *= ex2a;    RU[0][1] *= exab;  
+    RU[1][0] *= exab;    RU[1][1] *= ex2b;  
+    
+    TD[0][0] *= exa;     TD[0][1] *= exa; 
+    TD[1][0] *= exb;     TD[1][1] *= exb;
+
+    TU[0][0] *= exa;     TU[0][1] *= exb; 
+    TU[1][0] *= exa;     TU[1][1] *= exb;
+
+    *RUL *= ex2b;
+    *TDL *= exb;
+    *TUL *= exb;
+}
+
+
+
+
+void get_qwv(
+    bool ircvup, 
+    const MYCOMPLEX R1[2][2], MYCOMPLEX RL1, 
+    const MYCOMPLEX R2[2][2], MYCOMPLEX RL2, 
+    const MYCOMPLEX coef[3][2], MYCOMPLEX qwv[3])
+{
+    MYCOMPLEX qw0[2], qw1[2], v0;
+    MYCOMPLEX coefD[2] = {coef[0][0], coef[1][0]};
+    MYCOMPLEX coefU[2] = {coef[0][1], coef[1][1]};
+    if(ircvup){
+        cmat2x1_mul(R2, coefD, qw0);
+        qw0[0] += coefU[0]; qw0[1] += coefU[1]; 
+        v0 = RL1 * (RL2*coef[2][0] + coef[2][1]);
+    } else {
+        cmat2x1_mul(R2, coefU, qw0);
+        qw0[0] += coefD[0]; qw0[1] += coefD[1]; 
+        v0 = RL1 * (coef[2][0] + RL2*coef[2][1]);
+    }
+    cmat2x1_mul(R1, qw0, qw1);
+
+    qwv[0] = qw1[0];
+    qwv[1] = qw1[1];
+    qwv[2] = v0;
+}
+
diff --git a/pygrt/C_extension/src/dynamic/grt.c b/pygrt/C_extension/src/dynamic/grt.c
index 0a38c336..f2d98684 100755
--- a/pygrt/C_extension/src/dynamic/grt.c
+++ b/pygrt/C_extension/src/dynamic/grt.c
@@ -21,11 +21,12 @@
 #include <omp.h>
 
 #include "dynamic/grt.h"
-#include "dynamic/dwm.h"
 #include "dynamic/propagate.h"
-#include "dynamic/fim.h"
-#include "dynamic/ptam.h"
-#include "dynamic/iostats.h"
+#include "common/ptam.h"
+#include "common/fim.h"
+#include "common/dwm.h"
+#include "common/integral.h"
+#include "common/iostats.h"
 #include "common/const.h"
 #include "common/model.h"
 #include "common/prtdbg.h"
@@ -627,7 +628,7 @@ void integ_grn_spec(
             ptam_fstats[ir] = NULL;
             if(statsstr!=NULL && ((findElement_MYINT(statsidxs, nstatsidxs, iw) >= 0) || (findElement_MYINT(statsidxs, nstatsidxs, -1) >= 0))){
                 char *fname = (char *)malloc((strlen(fstatsdir[ir])+200)*sizeof(char));
-                if(Length > 0){
+                if(Length > RZERO){
                     // 常规的波数积分
                     sprintf(fname, "%s/K_%d_%.3e", fstatsdir[ir], iw, freqs[iw]);
                 } else {
@@ -637,7 +638,7 @@ void integ_grn_spec(
                 
                 fstats[ir] = fopen(fname, "wb");
 
-                if(vmin_ref < 0){
+                if(vmin_ref < RZERO){
                     // 峰谷平均法
                     sprintf(fname, "%s/PTAM_%d_%.3e", fstatsdir[ir], iw, freqs[iw]);
                     ptam_fstats[ir] = fopen(fname, "wb");
@@ -663,7 +664,7 @@ void integ_grn_spec(
                 calc_upar,
                 sum_EXP_uiz_J, sum_VF_uiz_J, sum_HF_uiz_J, sum_DC_uiz_J,
                 sum_EXP_uir_J, sum_VF_uir_J, sum_HF_uir_J, sum_DC_uir_J,
-                fstats);
+                fstats, kernel);
         } 
         else {
             // 基于线性插值的Filon积分
@@ -673,7 +674,7 @@ void integ_grn_spec(
                 calc_upar,
                 sum_EXP_uiz_J, sum_VF_uiz_J, sum_HF_uiz_J, sum_DC_uiz_J,
                 sum_EXP_uir_J, sum_VF_uir_J, sum_HF_uir_J, sum_DC_uir_J,
-                fstats);
+                fstats, kernel);
         }
 
         // k之后的部分使用峰谷平均法进行显式收敛，建议在浅源地震的时候使用   
@@ -684,7 +685,7 @@ void integ_grn_spec(
                 calc_upar,
                 sum_EXP_uiz_J, sum_VF_uiz_J, sum_HF_uiz_J, sum_DC_uiz_J,
                 sum_EXP_uir_J, sum_VF_uir_J, sum_HF_uir_J, sum_DC_uir_J,
-                fstats, ptam_fstats);
+                fstats, ptam_fstats, kernel);
         }
 
         // printf("iw=%d, w=%.5e, k=%.5e, dk=%.5e, nk=%d\n", iw, w, k, dk, (int)(k/dk));
diff --git a/pygrt/C_extension/src/dynamic/grt_main.c b/pygrt/C_extension/src/dynamic/grt_main.c
index 806ced25..bf672b03 100644
--- a/pygrt/C_extension/src/dynamic/grt_main.c
+++ b/pygrt/C_extension/src/dynamic/grt_main.c
@@ -86,8 +86,8 @@ bool iwk0=false;
 // 参考最小速度，小于0表示使用峰谷平均法;
 static double vmin_ref=0.0;
 static const double min_vmin_ref=0.1;
-// 自动使用峰谷平均法的最小厚度差 1km
-static const double hs_ptam = 0.5;
+// 自动使用峰谷平均法的最小厚度差
+static const double hs_ptam = MIN_DEPTH_GAP_SRC_RCV;
 // 时间延迟量，延迟参考速度。总延迟=T0 + dist/V0;
 static double delayT=0.0, delayT0=0.0, delayV0=0.0;
 static double tmax; // 时窗最大截止时刻
@@ -612,20 +612,18 @@ static void getopt_from_command(int argc, char **argv){
         fprintf(stderr, "[%s] " BOLD_RED "Error! Need set -R. Use '-h' for help.\n" DEFAULT_RESTORE, command);
         exit(EXIT_FAILURE);
     }
+    if(O_flag == 0){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set -O. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
 
 
-    if(O_flag == 1){
-        // 建立保存目录
-        if(mkdir(s_output_dir, 0777) != 0){
-            if(errno != EEXIST){
-                fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to create folder %s. Error code: %d\n" DEFAULT_RESTORE, command, s_output_dir, errno);
-                exit(EXIT_FAILURE);
-            }
+    // 建立保存目录
+    if(mkdir(s_output_dir, 0777) != 0){
+        if(errno != EEXIST){
+            fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to create folder %s. Error code: %d\n" DEFAULT_RESTORE, command, s_output_dir, errno);
+            exit(EXIT_FAILURE);
         }
-    } else {
-        // 使用当前目录
-        s_output_dir = (char*)malloc(sizeof(char)*100);
-        strcpy(s_output_dir, ".");
     }
     
 
@@ -1148,6 +1146,10 @@ int main(int argc, char **argv) {
     hd.user3 = pymod->Rho[pymod->ircv];
     hd.user4 = RONE/pymod->Qa[pymod->ircv];
     hd.user5 = RONE/pymod->Qb[pymod->ircv];
+    // 写入震源点的Vp,Vs,rho
+    hd.user6 = pymod->Va[pymod->isrc];
+    hd.user7 = pymod->Vb[pymod->isrc];
+    hd.user8 = pymod->Rho[pymod->isrc];
 
     
     // 做反傅里叶变换，保存SAC文件
@@ -1194,21 +1196,21 @@ int main(int argc, char **argv) {
                 if(doEXP){
                     write_one_to_sac("EX", chs[i], &hd, s_outpath, s_output_subdir, s_prefix, sgn, EXPcplx[ir][i], fftw_grn, out, float_arr, plan);
                     if(calc_upar){
-                        write_one_to_sac("EX", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn, EXPcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
+                        write_one_to_sac("EX", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn*(-1), EXPcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
                         write_one_to_sac("EX", chs[i], &hd, s_outpath, s_output_subdir, "r", sgn, EXPcplx_uir[ir][i], fftw_grn, out, float_arr, plan);
                     }
                 }
                 if(doVF){
                     write_one_to_sac("VF", chs[i], &hd, s_outpath, s_output_subdir, s_prefix, sgn, VFcplx[ir][i], fftw_grn, out, float_arr, plan);
                     if(calc_upar){
-                        write_one_to_sac("VF", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn, VFcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
+                        write_one_to_sac("VF", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn*(-1), VFcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
                         write_one_to_sac("VF", chs[i], &hd, s_outpath, s_output_subdir, "r", sgn, VFcplx_uir[ir][i], fftw_grn, out, float_arr, plan);
                     }
                 }
                 if(doDC){
                     write_one_to_sac("DD", chs[i], &hd, s_outpath, s_output_subdir, s_prefix, sgn, DDcplx[ir][i], fftw_grn, out, float_arr, plan);
                     if(calc_upar){
-                        write_one_to_sac("DD", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn, DDcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
+                        write_one_to_sac("DD", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn*(-1), DDcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
                         write_one_to_sac("DD", chs[i], &hd, s_outpath, s_output_subdir, "r", sgn, DDcplx_uir[ir][i], fftw_grn, out, float_arr, plan);
                     }
                 }
@@ -1217,7 +1219,7 @@ int main(int argc, char **argv) {
             if(doHF){
                 write_one_to_sac("HF", chs[i], &hd, s_outpath, s_output_subdir, s_prefix, sgn, HFcplx[ir][i], fftw_grn, out, float_arr, plan);
                 if(calc_upar){
-                    write_one_to_sac("HF", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn, HFcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
+                    write_one_to_sac("HF", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn*(-1), HFcplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
                     write_one_to_sac("HF", chs[i], &hd, s_outpath, s_output_subdir, "r", sgn, HFcplx_uir[ir][i], fftw_grn, out, float_arr, plan);
                 }
             }
@@ -1225,12 +1227,12 @@ int main(int argc, char **argv) {
             if(doDC){
                 write_one_to_sac("DS", chs[i], &hd, s_outpath, s_output_subdir, s_prefix, sgn, DScplx[ir][i], fftw_grn, out, float_arr, plan);
                 if(calc_upar){
-                    write_one_to_sac("DS", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn, DScplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
+                    write_one_to_sac("DS", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn*(-1), DScplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
                     write_one_to_sac("DS", chs[i], &hd, s_outpath, s_output_subdir, "r", sgn, DScplx_uir[ir][i], fftw_grn, out, float_arr, plan);
                 }
                 write_one_to_sac("SS", chs[i], &hd, s_outpath, s_output_subdir, s_prefix, sgn, SScplx[ir][i], fftw_grn, out, float_arr, plan);
                 if(calc_upar){
-                    write_one_to_sac("SS", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn, SScplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
+                    write_one_to_sac("SS", chs[i], &hd, s_outpath, s_output_subdir, "z", sgn*(-1), SScplx_uiz[ir][i], fftw_grn, out, float_arr, plan);
                     write_one_to_sac("SS", chs[i], &hd, s_outpath, s_output_subdir, "r", sgn, SScplx_uir[ir][i], fftw_grn, out, float_arr, plan);
                 }
             }
diff --git a/pygrt/C_extension/src/dynamic/grt_strain.c b/pygrt/C_extension/src/dynamic/grt_strain.c
index ecd0bc26..03336cb9 100644
--- a/pygrt/C_extension/src/dynamic/grt_strain.c
+++ b/pygrt/C_extension/src/dynamic/grt_strain.c
@@ -14,6 +14,7 @@
 #include <dirent.h>
 #include <ctype.h>
 #include <string.h>
+#include <stdbool.h>
 
 #include "common/sacio2.h"
 #include "common/const.h"
@@ -21,6 +22,18 @@
 #include "common/colorstr.h"
 
 
+//****************** 在该文件以内的全局变量 ***********************//
+// 命令名称
+static char *command = NULL;
+
+// 输出分量格式，即是否需要旋转到ZNE
+static bool rot2ZNE = false;
+
+// 三分量
+const char zrtchs[3] = {'Z', 'R', 'T'};
+const char znechs[3] = {'Z', 'N', 'E'};
+const char *chs = NULL;
+
 
 /**
  * 打印使用说明
@@ -39,27 +52,51 @@ printf("\n"
 }
 
 
-
-int main(int argc, char **argv){
-    const char *command = argv[0];
-
-    // 输入不够
-    if(argc < 2){
-        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set an input. Use '-h' for help.\n" DEFAULT_RESTORE, command);
-        exit(EXIT_FAILURE);
+/**
+ * 从命令行中读取选项，处理后记录到全局变量中
+ * 
+ * @param     argc      命令行的参数个数
+ * @param     argv      多个参数字符串指针
+ */
+static void getopt_from_command(int argc, char **argv){
+    int opt;
+    while ((opt = getopt(argc, argv, ":h")) != -1) {
+        switch (opt) {
+
+            // 帮助
+            case 'h':
+                print_help();
+                exit(EXIT_SUCCESS);
+                break;
+
+            // 参数缺失
+            case ':':
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' requires an argument. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+
+            // 非法选项
+            case '?':
+            default:
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' is invalid. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+        }
     }
 
-    // 输入过多
-    if(argc > 2){
-        fprintf(stderr, "[%s] " BOLD_RED "Error! You should set only one input. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+    // 检查必选项有没有设置
+    if(argc != 2){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set options. Use '-h' for help.\n" DEFAULT_RESTORE, command);
         exit(EXIT_FAILURE);
     }
+}
 
-    // 使用-h查看帮助
-    if(strcmp(argv[1], "-h") == 0){
-        print_help();
-        exit(EXIT_SUCCESS);
-    }
+
+
+int main(int argc, char **argv){
+    command = argv[0];
+
+    getopt_from_command(argc, argv);
 
     
     // 合成地震图目录路径
@@ -78,18 +115,24 @@ int main(int argc, char **argv){
         exit(EXIT_FAILURE);
     } 
 
-    
 
     // ----------------------------------------------------------------------------------
     // 开始读取计算，输出6个量
     float *arrin = NULL;
     char c1, c2;
     char *s_filepath = (char*)malloc(sizeof(char) * (strlen(s_synpath)+strlen(s_prefix)+100));
-    const char chs[3] = {'R', 'T', 'Z'};
+
+    // 判断标志性文件是否存在，来判断输出使用ZNE还是ZRT
+    sprintf(s_filepath, "%s/n%sN.sac", s_synpath, s_prefix);
+    rot2ZNE = (access(s_filepath, F_OK) == 0);
+
+    // 指示特定的通道名
+    chs = (rot2ZNE)? znechs : zrtchs;
+
 
     // 读取一个头段变量，获得基本参数，分配数组内存
     SACHEAD hd;
-    sprintf(s_filepath, "%s/r%sR.sac", s_synpath, s_prefix);
+    sprintf(s_filepath, "%s/%c%s%c.sac", s_synpath, tolower(chs[0]), s_prefix, chs[0]);
     read_SAC_HEAD(command, s_filepath, &hd);
     int npts=hd.npts;
     float dist=hd.dist;
@@ -116,18 +159,18 @@ int main(int argc, char **argv){
             // 累加
             for(int i=0; i<npts; ++i)  arrout[i] = (arrout[i] + arrin[i]) * 0.5f;
 
-            // 特殊情况需加上协变导数
+            // 特殊情况需加上协变导数，1e-5是因为km->cm
             if(c1=='R' && c2=='T'){
                 // 读取数据 u_T
                 sprintf(s_filepath, "%s/%sT.sac", s_synpath, s_prefix);
                 arrin = read_SAC(command, s_filepath, &hd, arrin);
-                for(int i=0; i<npts; ++i)  arrout[i] -= 0.5f * arrin[i] / dist;
+                for(int i=0; i<npts; ++i)  arrout[i] -= 0.5f * arrin[i] / dist * 1e-5;
             }
             else if(c1=='T' && c2=='T'){
                 // 读取数据 u_R
                 sprintf(s_filepath, "%s/%sR.sac", s_synpath, s_prefix);
                 arrin = read_SAC(command, s_filepath, &hd, arrin);
-                for(int i=0; i<npts; ++i)  arrout[i] += arrin[i] / dist;
+                for(int i=0; i<npts; ++i)  arrout[i] += arrin[i] / dist * 1e-5;
             }
 
             // 保存到SAC
diff --git a/pygrt/C_extension/src/dynamic/grt_stress.c b/pygrt/C_extension/src/dynamic/grt_stress.c
index 6af27425..b4732cb0 100644
--- a/pygrt/C_extension/src/dynamic/grt_stress.c
+++ b/pygrt/C_extension/src/dynamic/grt_stress.c
@@ -13,6 +13,7 @@
 #include <dirent.h>
 #include <ctype.h>
 #include <string.h>
+#include <stdbool.h>
 
 #include <complex.h>
 #include <fftw3.h>
@@ -23,6 +24,20 @@
 #include "common/logo.h"
 #include "common/colorstr.h"
 
+
+//****************** 在该文件以内的全局变量 ***********************//
+// 命令名称
+static char *command = NULL;
+
+// 输出分量格式，即是否需要旋转到ZNE
+static bool rot2ZNE = false;
+
+// 三分量
+const char zrtchs[3] = {'Z', 'R', 'T'};
+const char znechs[3] = {'Z', 'N', 'E'};
+const char *chs = NULL;
+
+
 /**
  * 打印使用说明
  */
@@ -31,6 +46,7 @@ print_logo();
 printf("\n"
 "[grt.stress]\n\n"
 "    Conbine spatial derivatives of displacements into stress.\n"
+"    (unit: dyne/cm^2 = 0.1 Pa)\n"
 "\n\n"
 "Usage:\n"
 "----------------------------------------------------------------\n"
@@ -40,27 +56,51 @@ printf("\n"
 }
 
 
-
-int main(int argc, char **argv){
-    const char *command = argv[0];
-
-    // 输入不够
-    if(argc < 2){
-        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set an input. Use '-h' for help.\n" DEFAULT_RESTORE, command);
-        exit(EXIT_FAILURE);
+/**
+ * 从命令行中读取选项，处理后记录到全局变量中
+ * 
+ * @param     argc      命令行的参数个数
+ * @param     argv      多个参数字符串指针
+ */
+static void getopt_from_command(int argc, char **argv){
+    int opt;
+    while ((opt = getopt(argc, argv, ":h")) != -1) {
+        switch (opt) {
+
+            // 帮助
+            case 'h':
+                print_help();
+                exit(EXIT_SUCCESS);
+                break;
+
+            // 参数缺失
+            case ':':
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' requires an argument. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+
+            // 非法选项
+            case '?':
+            default:
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' is invalid. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+        }
     }
 
-    // 输入过多
-    if(argc > 2){
-        fprintf(stderr, "[%s] " BOLD_RED "Error! You should set only one input. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+    // 检查必选项有没有设置
+    if(argc != 2){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set options. Use '-h' for help.\n" DEFAULT_RESTORE, command);
         exit(EXIT_FAILURE);
     }
+}
 
-    // 使用-h查看帮助
-    if(strcmp(argv[1], "-h") == 0){
-        print_help();
-        exit(EXIT_SUCCESS);
-    }
+
+
+int main(int argc, char **argv){
+    command = argv[0];
+
+    getopt_from_command(argc, argv);
 
     
     // 合成地震图目录路径
@@ -84,11 +124,18 @@ int main(int argc, char **argv){
     // 开始读取计算，输出6个量
     char c1, c2;
     char *s_filepath = (char*)malloc(sizeof(char) * (strlen(s_synpath)+strlen(s_prefix)+100));
-    const char chs[3] = {'R', 'T', 'Z'};
+
+    // 判断标志性文件是否存在，来判断输出使用ZNE还是ZRT
+    sprintf(s_filepath, "%s/n%sN.sac", s_synpath, s_prefix);
+    rot2ZNE = (access(s_filepath, F_OK) == 0);
+
+    // 指示特定的通道名
+    chs = (rot2ZNE)? znechs : zrtchs;
+
 
     // 读取一个头段变量，获得基本参数，分配数组内存
     SACHEAD hd;
-    sprintf(s_filepath, "%s/r%sR.sac", s_synpath, s_prefix);
+    sprintf(s_filepath, "%s/%c%s%c.sac", s_synpath, tolower(chs[0]), s_prefix, chs[0]);
     read_SAC_HEAD(command, s_filepath, &hd);
     int npts=hd.npts;
     float dt=hd.delta;
@@ -100,7 +147,7 @@ int main(int argc, char **argv){
     float rho=hd.user3;
     float Qainv=hd.user4;
     float Qbinv=hd.user5;
-    if(va < 0.0 || vb < 0.0 || rho < 0.0){
+    if(va <= 0.0 || vb <= 0.0 || rho <= 0.0){
         fprintf(stderr, "[%s] " BOLD_RED "Error! read necessary header value from \"%s\" error.\n" DEFAULT_RESTORE, command, s_filepath);
         exit(EXIT_FAILURE);
     }
@@ -132,8 +179,9 @@ int main(int argc, char **argv){
         fftwf_complex atta, attb;
         atta = attenuation_law(Qainv, w);
         attb = attenuation_law(Qbinv, w);
-        mus[i] = vb*vb*attb*attb*rho;
-        lams[i] = va*va*atta*atta*rho - 2.0*mus[i];
+        // 乘上1e10，转为dyne/(cm^2)
+        mus[i] = vb*vb*attb*attb*rho*1e10;
+        lams[i] = va*va*atta*atta*rho*1e10 - 2.0*mus[i];
     }
 
     // ----------------------------------------------------------------------------------
@@ -150,10 +198,12 @@ int main(int argc, char **argv){
         for(int i=0; i<nf; ++i)  lam_ukk[i] += carrin[i];
     }
     // 加上协变导数
-    sprintf(s_filepath, "%s/%sR.sac", s_synpath, s_prefix);
-    arrin = read_SAC(command, s_filepath, &hd, arrin);
-    fftwf_execute(plan);
-    for(int i=0; i<nf; ++i)  lam_ukk[i] += carrin[i]/dist;
+    if(!rot2ZNE){
+        sprintf(s_filepath, "%s/%sR.sac", s_synpath, s_prefix);
+        arrin = read_SAC(command, s_filepath, &hd, arrin);
+        fftwf_execute(plan);
+        for(int i=0; i<nf; ++i)  lam_ukk[i] += carrin[i]/dist*1e-5;
+    }
 
     // 乘上lambda系数
     for(int i=0; i<nf; ++i)  lam_ukk[i] *= lams[i];
@@ -194,20 +244,20 @@ int main(int argc, char **argv){
                 for(int i=0; i<nf; ++i)  carrout[i] += lam_ukk[i];
             }
 
-            // 特殊情况需加上协变导数
+            // 特殊情况需加上协变导数，1e-5是因为km->cm
             if(c1=='R' && c2=='T'){
                 // 读取数据 u_T
                 sprintf(s_filepath, "%s/%sT.sac", s_synpath, s_prefix);
                 arrin = read_SAC(command, s_filepath, &hd, arrin);
                 fftwf_execute(plan);
-                for(int i=0; i<nf; ++i)  carrout[i] -= mus[i] * carrin[i] / dist;
+                for(int i=0; i<nf; ++i)  carrout[i] -= mus[i] * carrin[i] / dist * 1e-5;
             }
             else if(c1=='T' && c2=='T'){
                 // 读取数据 u_R
                 sprintf(s_filepath, "%s/%sR.sac", s_synpath, s_prefix);
                 arrin = read_SAC(command, s_filepath, &hd, arrin);
                 fftwf_execute(plan);
-                for(int i=0; i<nf; ++i)  carrout[i] += 2.0f * mus[i] * carrin[i] / dist;
+                for(int i=0; i<nf; ++i)  carrout[i] += 2.0f * mus[i] * carrin[i] / dist * 1e-5;
             }
             
             // 保存到SAC
diff --git a/pygrt/C_extension/src/dynamic/grt_syn.c b/pygrt/C_extension/src/dynamic/grt_syn.c
index 399c13e5..5d2b4545 100644
--- a/pygrt/C_extension/src/dynamic/grt_syn.c
+++ b/pygrt/C_extension/src/dynamic/grt_syn.c
@@ -19,18 +19,17 @@
 #include <math.h>
 #include <stdbool.h>
 #include <dirent.h>
+#include <ctype.h>
 
 #include "dynamic/signals.h"
 #include "common/sacio2.h"
 #include "common/const.h"
 #include "common/logo.h"
 #include "common/colorstr.h"
+#include "common/radiation.h"
+#include "common/coord.h"
+
 
-#define DEG1 0.017453292519943295
-#define COMPUTE_EXP 0
-#define COMPUTE_SF 1
-#define COMPUTE_DC 2
-#define COMPUTE_MT 3
 
 extern char *optarg;
 extern int optind;
@@ -49,18 +48,14 @@ static char *s_prefix = NULL;
 static const char *s_prefix_default = "out";
 // 方位角，以及对应弧度制
 static double azimuth = 0.0, azrad = 0.0, backazimuth=0.0;
-static double caz = 0.0, saz = 0.0;
-static double caz2 = 0.0, saz2 = 0.0;
 // 放大系数，对于位错源、爆炸源、张量震源，M0是标量地震矩；对于单力源，M0是放大系数
 static double M0 = 0.0;
-// 位错震源机制
-static double strike=0.0, dip=0.0, rake=0.0;
-// 单力源
-static double fn=0.0, fe=0.0, fz=0.0;
-// 张量震源 
-static double Mxx=0.0, Mxy=0.0, Mxz=0.0, Myy=0.0, Myz=0.0, Mzz=0.0;
+// 在放大系数上是否需要乘上震源处的剪切模量
+static bool mult_src_mu = false;
+// 存储不同震源的震源机制相关参数的数组
+static double mchn[6] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
 // 最终要计算的震源类型
-static int computeType=COMPUTE_EXP;
+static int computeType=GRT_SYN_COMPUTE_EX;
 static char s_computeType[3] = "EX";
 // 和宏命令对应的震源类型全称
 static const char *sourceTypeFullName[] = {"Explosion", "Single Force", "Double Couple", "Moment Tensor"};
@@ -75,22 +70,26 @@ static int dif_times = 0;
 // 是否计算位移空间导数
 static bool calc_upar=false;
 
+// 输出分量格式，即是否需要旋转到ZNE
+static bool rot2ZNE = false;
+
 // 各选项的标志变量，初始化为0，定义了则为1
 static int G_flag=0, O_flag=0, A_flag=0,
            S_flag=0, M_flag=0, F_flag=0,
            T_flag=0, P_flag=0, s_flag=0,
            D_flag=0, I_flag=0, J_flag=0, 
-           e_flag=0;
+           e_flag=0, N_flag=0;
 
 // 三分量代号
-static const char chs[3] = {'Z', 'R', 'T'};
+static const char zrtchs[3] = {'Z', 'R', 'T'};
+static const char znechs[3] = {'Z', 'N', 'E'};
 
 // 计算和位移相关量的种类（1-位移，2-ui_z，3-ui_r，4-ui_t）
 static int calcUTypes=1;
 
 // 文件名前缀，分别用于合成，1-位移，2-ui_z，3-ui_r，4-ui_t。顺序不能更改
 static const char sacin_prefixes[4][2] = {"", "z", "r", ""};  // 输入文件
-static const char sacout_prefixes[4][2] = {"", "z", "r", "t"}; // 输出文件
+static char sacout_prefixes[4][2] = {"", "z", "r", "t"}; // 输出文件
 
 // 震源名称数组，以及方向因子数组
 static const int srcnum = 6;
@@ -105,6 +104,9 @@ static double srcCoef[3][6] = { // 三分量和chs数组对应
 static char tftype = GRT_SIG_CUSTOM;
 static char *tfparams = NULL;
 
+// 震源处的剪切模量
+static double src_mu = 0.0;
+
 
 
 
@@ -116,12 +118,12 @@ print_logo();
 printf("\n"
 "[grt.syn]\n\n"
 "    A Supplementary Tool of GRT to Compute Three-Component \n"
-"    Displacement with the Green's Functions from command `grt`.\n"
+"    Displacement with the outputs of command `grt`.\n"
 "    Three components are:\n"
 "       + Up (Z),\n"
 "       + Radial Outward (R),\n"
 "       + Transverse Clockwise (T),\n"
-"    and the units are cm.\n"
+"    and the units are cm. You can add -N to rotate ZRT to ZNE.\n"
 "\n"
 "    + Default outputs (without -I and -J) are impulse-like displacements.\n"
 "    + -D, -I and -J are applied in the frequency domain.\n"
@@ -133,7 +135,7 @@ printf("\n"
 "            [-T<Mxx>/<Mxy>/<Mxz>/<Myy>/<Myz>/<Mzz>]\n"
 "            [-F<fn>/<fe>/<fz>] \n"
 "            [-D<tftype>/<tfparams>] [-I<odr>] [-J<odr>]\n" 
-"            [-P<prefix>] [-e] [-s]\n"
+"            [-P<prefix>] [-N] [-e] [-s]\n"
 "\n"
 "\n\n"
 "Options:\n"
@@ -201,6 +203,8 @@ printf("\n"
 "\n"
 "    -J<odr>       Order of differentiation. Default not use\n"
 "\n"
+"    -N            Components of results will be Z, N, E.\n"
+"\n"
 "    -e            Compute the spatial derivatives, ui_z and ui_r,\n"
 "                  of displacement u. In filenames, prefix \"r\" means \n"
 "                  ui_r and \"z\" means ui_z. \n"
@@ -243,6 +247,19 @@ static void check_grn_exist(const char *name){
         fprintf(stderr, "[%s] " BOLD_RED "Error! %s not exists.\n" DEFAULT_RESTORE, command, buffer);
         exit(EXIT_FAILURE);
     }
+    // 检查文件的同时将src_mu计算出来
+    if(src_mu == 0.0){
+        SACHEAD hd;
+        read_SAC_HEAD(command, buffer, &hd);
+        double vb, rho;
+        vb = hd.user7;
+        rho = hd.user8;
+        if(vb <= 0.0 || rho <= 0.0){
+            fprintf(stderr, "[%s] " BOLD_RED "Error! read necessary header value from \"%s\" error.\n" DEFAULT_RESTORE, command, buffer);
+            exit(EXIT_FAILURE);
+        }
+        src_mu = vb*vb*rho*1e10;
+    }
     free(buffer);
 }
 
@@ -255,7 +272,7 @@ static void check_grn_exist(const char *name){
  */
 static void getopt_from_command(int argc, char **argv){
     int opt;
-    while ((opt = getopt(argc, argv, ":G:A:S:M:F:T:O:P:D:I:J:ehs")) != -1) {
+    while ((opt = getopt(argc, argv, ":G:A:S:M:F:T:O:P:D:I:J:Nehs")) != -1) {
         switch (opt) {
             // 格林函数路径
             case 'G':
@@ -285,15 +302,20 @@ static void getopt_from_command(int argc, char **argv){
                 backazimuth = 180.0 + azimuth;
                 if(backazimuth >= 360.0) backazimuth -= 360.0;
                 azrad = azimuth * DEG1;
-                saz = sin(azrad);
-                caz = cos(azrad);
-                saz2 = 2.0*saz*caz;
-                caz2 = 2.0*caz*caz - 1.0;
                 break;
 
             // 放大系数
             case 'S':
                 S_flag = 1;
+                {   
+                    // 检查是否存在字符u，若存在表明需要乘上震源处的剪切模量
+                    char *upos=NULL;
+                    if((upos=strchr(optarg, 'u')) != NULL){
+                        mult_src_mu = true;
+                        *upos = ' ';
+                    }
+                }
+                
                 if(0 == sscanf(optarg, "%lf", &M0)){
                     fprintf(stderr, "[%s] " BOLD_RED "Error in -S.\n" DEFAULT_RESTORE, command);
                     exit(EXIT_FAILURE);
@@ -303,7 +325,8 @@ static void getopt_from_command(int argc, char **argv){
             // 位错震源
             case 'M':
                 M_flag = 1; 
-                computeType = COMPUTE_DC;
+                computeType = GRT_SYN_COMPUTE_DC;
+                double strike, dip, rake;
                 sprintf(s_computeType, "%s", "DC");
                 if(3 != sscanf(optarg, "%lf/%lf/%lf", &strike, &dip, &rake)){
                     fprintf(stderr, "[%s] " BOLD_RED "Error in -M.\n" DEFAULT_RESTORE, command);
@@ -321,28 +344,42 @@ static void getopt_from_command(int argc, char **argv){
                     fprintf(stderr, "[%s] " BOLD_RED "Error! Rake in -M must be in [-180, 180].\n" DEFAULT_RESTORE, command);
                     exit(EXIT_FAILURE);
                 }
+                mchn[0] = strike;
+                mchn[1] = dip;
+                mchn[2] = rake;
                 break;
 
             // 单力源
             case 'F':
                 F_flag = 1;
-                computeType = COMPUTE_SF;
+                computeType = GRT_SYN_COMPUTE_SF;
+                double fn, fe, fz;
                 sprintf(s_computeType, "%s", "SF");
                 if(3 != sscanf(optarg, "%lf/%lf/%lf", &fn, &fe, &fz)){
                     fprintf(stderr, "[%s] " BOLD_RED "Error in -F.\n" DEFAULT_RESTORE, command);
                     exit(EXIT_FAILURE);
                 };
+                mchn[0] = fn;
+                mchn[1] = fe;
+                mchn[2] = fz;
                 break;
 
             // 张量震源
             case 'T':
                 T_flag = 1;
-                computeType = COMPUTE_MT;
+                computeType = GRT_SYN_COMPUTE_MT;
+                double Mxx, Mxy, Mxz, Myy, Myz, Mzz;
                 sprintf(s_computeType, "%s", "MT");
                 if(6 != sscanf(optarg, "%lf/%lf/%lf/%lf/%lf/%lf", &Mxx, &Mxy, &Mxz, &Myy, &Myz, &Mzz)){
                     fprintf(stderr, "[%s] " BOLD_RED "Error in -T.\n" DEFAULT_RESTORE, command);
                     exit(EXIT_FAILURE);
                 };
+                mchn[0] = Mxx;
+                mchn[1] = Mxy;
+                mchn[2] = Mxz;
+                mchn[3] = Myy;
+                mchn[4] = Myz;
+                mchn[5] = Mzz;
                 break;
 
             // 输出路径
@@ -407,6 +444,12 @@ static void getopt_from_command(int argc, char **argv){
                 calcUTypes = 4;
                 break;
 
+            // 是否旋转到ZNE
+            case 'N':
+                N_flag = 1;
+                rot2ZNE = true;
+                break;
+
             // 不打印在终端
             case 's':
                 s_flag = 1;
@@ -520,6 +563,8 @@ static void getopt_from_command(int argc, char **argv){
         s_prefix = (char*)malloc(sizeof(char)*100);
         strcpy(s_prefix, s_prefix_default);
     }
+
+    if(mult_src_mu)  M0 *= src_mu;
 }
 
 
@@ -554,89 +599,46 @@ static void save_tf_to_sac(char *buffer, float *tfarr, int tfnt, float dt){
     write_sac(buffer, hd, tfarr);
 }
 
+
 /**
- * 设置每个震源的方向因子
+ * 将不同ZRT分量的位移以及位移空间导数旋转到ZNE分量
  * 
- * @param      par_theta       方向因子中是否对theta(az)求导
- * @param      coef            缩放系数，用于位移空间导数的计算
+ * @param    syn       位移
+ * @param    syn_upar  位移空间导数
+ * @param    nt        时间点数
+ * @param    azrad     方位角弧度
+ * @param    dist      震中距(km)
  */
-static void set_source_coef(const bool par_theta, const double coef){
-    double mult;
-    if(computeType == COMPUTE_SF){
-        mult = 1e-15*M0*coef;
-    } else {
-        mult = 1e-20*M0*coef;
-    }
+static void data_zrt2zne(float *syn[3], float *syn_upar[3][3], int nt, double azrad, double dist){
+    double dblsyn[3];
+    double dblupar[3][3];
+
+    bool doupar = (syn_upar[0][0]!=NULL);
+
+    // 对每一个时间点
+    for(int n=0; n<nt; ++n){
+        // 复制数据，以调用函数
+        for(int i1=0; i1<3; ++i1){
+            dblsyn[i1] = syn[i1][n];
+            for(int i2=0; i2<3; ++i2){
+                if(doupar) dblupar[i1][i2] = syn_upar[i1][i2][n];
+            }
+        }
 
-    if(computeType == COMPUTE_EXP){
-        srcCoef[0][0] = srcCoef[1][0] = (par_theta)? 0.0 : mult; // Z/R
-        srcCoef[2][0] = 0.0; // T
-    }  
-    else if(computeType == COMPUTE_SF){
-        double A0, A1, A4;
-        A0 = fz*mult;
-        A1 = (fn*caz + fe*saz)*mult;
-        A4 = (- fn*saz + fe*caz)*mult;
-
-        // 公式(4.6.20)
-        srcCoef[0][1] = srcCoef[1][1] = (par_theta)? 0.0 : A0; // VF, Z/R
-        srcCoef[0][2] = srcCoef[1][2] = (par_theta)? A4 : A1; // HF, Z/R
-        srcCoef[2][1] = 0.0; // VF, T
-        srcCoef[2][2] = (par_theta)? -A1 : A4; // HF, T
-    }
-    else if(computeType == COMPUTE_DC){
-        // 公式(4.8.35)
-        double stkrad = strike*DEG1;
-        double diprad = dip*DEG1;
-        double rakrad = rake*DEG1;
-        double therad = azrad - stkrad;
-        double srak, crak, sdip, cdip, sdip2, cdip2, sthe, cthe, sthe2, cthe2;
-        srak = sin(rakrad);     crak = cos(rakrad);
-        sdip = sin(diprad);     cdip = cos(diprad);
-        sdip2 = 2.0*sdip*cdip;  cdip2 = 2.0*cdip*cdip - 1.0;
-        sthe = sin(therad);     cthe = cos(therad);
-        sthe2 = 2.0*sthe*cthe;  cthe2 = 2.0*cthe*cthe - 1.0;
-
-        double A0, A1, A2, A4, A5;
-        A0 = mult * (0.5*sdip2*srak);
-        A1 = mult * (cdip*crak*cthe - cdip2*srak*sthe);
-        A2 = mult * (0.5*sdip2*srak*cthe2 + sdip*crak*sthe2);
-        A4 = mult * (- cdip2*srak*cthe - cdip*crak*sthe);
-        A5 = mult * (sdip*crak*cthe2 - 0.5*sdip2*srak*sthe2);
-
-        srcCoef[0][3] = srcCoef[1][3] = (par_theta)? 0.0 : A0; // DD, Z/R
-        srcCoef[0][4] = srcCoef[1][4] = (par_theta)? A4 : A1; // DS, Z/R
-        srcCoef[0][5] = srcCoef[1][5] = (par_theta)? 2.0*A5 : A2; // SS, Z/R
-        srcCoef[2][3] = 0.0; // DD, T
-        srcCoef[2][4] = (par_theta)? -A1 : A4;  // DS, T
-        srcCoef[2][5] = (par_theta)? -2.0*A2 : A5;  // DS, T
-    }
-    else if(computeType == COMPUTE_MT){
-        // 公式(4.9.7)但修改了各向同性的量
-        double M11, M12, M13, M22, M23, M33;
-        M11 = Mxx;   M12 = Mxy;   M13 = Mxz;
-                     M22 = Myy;   M23 = Myz;
-                                  M33 = Mzz;
-        double Mexp = (M11 + M22 + M33)/3.0;
-        M11 -= Mexp;
-        M22 -= Mexp;
-        M33 -= Mexp;
-
-        double A0, A1, A2, A4, A5;
-        A0 = mult * ((2.0*M33 - M11 - M22)/6.0 );
-        A1 = mult * (- (M13*caz + M23*saz));
-        A2 = mult * (0.5*(M11 - M22)*caz2+ M12*saz2);
-        A4 = mult * (M13*saz - M23*caz);
-        A5 = mult * (-0.5*(M11 - M22)*saz2 + M12*caz2);
-
-        srcCoef[0][0] = srcCoef[1][0] = (par_theta)? 0.0 : mult*Mexp; // EX, Z/R
-        srcCoef[0][3] = srcCoef[1][3] = (par_theta)? 0.0 : A0; // DD, Z/R
-        srcCoef[0][4] = srcCoef[1][4] = (par_theta)? A4 : A1; // DS, Z/R
-        srcCoef[0][5] = srcCoef[1][5] = (par_theta)? 2.0*A5 : A2; // SS, Z/R
-        srcCoef[2][0] = 0.0; // EX, T
-        srcCoef[2][3] = 0.0; // DD, T
-        srcCoef[2][4] = (par_theta)? -A1 : A4;  // DS, T
-        srcCoef[2][5] = (par_theta)? -2.0*A2 : A5;  // DS, T
+        if(doupar) {
+            rot_zrt2zxy_upar(azrad, dblsyn, dblupar, dist*1e5);
+        } else {
+            rot_zxy2zrt_vec(-azrad, dblsyn);
+        }
+        
+
+        // 将结果写入原数组
+        for(int i1=0; i1<3; ++i1){
+            syn[i1][n] = dblsyn[i1];
+            for(int i2=0; i2<3; ++i2){
+                if(doupar)  syn_upar[i1][i2][n] = dblupar[i1][i2];
+            }
+        }
     }
 }
 
@@ -649,10 +651,18 @@ int main(int argc, char **argv){
     command = argv[0];
     getopt_from_command(argc, argv);
 
+    // 根据参数设置，选择分量名
+    const char *chs = (rot2ZNE)? znechs : zrtchs;
+    for(int i=1; i<4; ++i){
+        sacout_prefixes[i][0] = tolower(chs[i-1]);
+    }
 
     char *buffer = (char*)malloc(sizeof(char)*(strlen(s_grnpath)+strlen(s_output_dir)+strlen(s_prefix)+100));
-    float *arr, *arrout=NULL, *convarr=NULL;
-    SACHEAD hd;
+    float **ptarrout=NULL, *arrout=NULL;
+    float *arrsyn[3] = {NULL, NULL, NULL};
+    float *arrsyn_upar[3][3] = {NULL};
+    SACHEAD hdsyn[3], hdsyn_upar[3][3], hd0;
+    SACHEAD *pthd=NULL;
     float *tfarr = NULL;
     int tfnt = 0;
     char ch;
@@ -689,28 +699,42 @@ int main(int argc, char **argv){
         }
         
         // 重新计算方向因子
-        set_source_coef((ityp==3), upar_scale);
+        set_source_radiation(srcCoef, computeType, (ityp==3), M0, upar_scale, azrad, mchn);
 
         for(int c=0; c<3; ++c){
-            ch = chs[c];
+            ch = zrtchs[c];
+            
+            // 定义SACHEAD指针
+            if(ityp==0){
+                pthd = &hdsyn[c];
+                ptarrout = &arrsyn[c];
+            } else {
+                pthd = &hdsyn_upar[ityp-1][c];
+                ptarrout = &arrsyn_upar[ityp-1][c];
+            }
+            arrout = *ptarrout;
+
             for(int k=0; k<srcnum; ++k){
                 coef = srcCoef[c][k];
                 if(coef == 0.0) continue;
     
                 sprintf(buffer, "%s/%s%s%c.sac", s_grnpath, sacin_prefixes[ityp], srcName[k], ch);
-                arr = read_SAC(command, buffer, &hd, NULL);
+                float *arr = read_SAC(command, buffer, pthd, NULL);
+                hd0 = *pthd; // 备份一份
 
-                nt = hd.npts;
-                dt = hd.delta;
-                dist = hd.dist;
+                nt = pthd->npts;
+                dt = pthd->delta;
+                dist = pthd->dist;
                 // dw = PI2/(nt*dt);
+
+                // 第一次读入元信息，申请数组内存
                 if(arrout==NULL){
-                    arrout = (float*)calloc(nt, sizeof(float));
+                    arrout = *ptarrout = (float*)calloc(nt, sizeof(float));
                 }    
     
                 // 使用虚频率将序列压制，卷积才会稳定
                 // 读入虚频率 
-                wI = hd.user0;
+                wI = pthd->user0;
                 fac = 1.0;
                 dfac = expf(-wI*dt);
                 for(int n=0; n<nt; ++n){
@@ -720,6 +744,13 @@ int main(int argc, char **argv){
     
                 free(arr);
             } // ENDFOR 不同震源
+            
+            // 再次检查内存，例如爆炸源的T分量，不会进入上述for循环，导致arrout没有分配内存
+            if(arrout==NULL){
+                arrout = *ptarrout = (float*)calloc(nt, sizeof(float));
+                *pthd = hd0;
+                continue;  // 直接跳过，认为这一分量全为0
+            }
     
             if(D_flag == 1 && tfarr==NULL){
                 // 获得时间函数 
@@ -735,10 +766,11 @@ int main(int argc, char **argv){
                     fac *= dfac;
                 }
             } 
+
     
             // 时域循环卷积
             if(tfarr!=NULL){
-                convarr = (float*)calloc(nt, sizeof(float));
+                float *convarr = (float*)calloc(nt, sizeof(float));
                 oaconvolve(arrout, nt, tfarr, tfnt, convarr, nt, true);
                 for(int i=0; i<nt; ++i){
                     arrout[i] = convarr[i] * dt; // dt是连续卷积的系数
@@ -762,17 +794,25 @@ int main(int argc, char **argv){
                 differential(arrout, nt, dt);
             }
     
-            // 保存成SAC文件
-            save_to_sac(buffer, sacout_prefixes[ityp], ch, arrout, hd);
-    
-            // 置零
-            for(int n=0; n<nt; ++n){
-                arrout[n] = 0.0f;
-            }
         } // ENDFOR 三分量
     }
     
 
+    // 是否需要旋转
+    if(rot2ZNE){
+        data_zrt2zne(arrsyn, arrsyn_upar, nt, azrad, dist);
+    }
+
+    // 保存到SAC文件
+    for(int i1=0; i1<3; ++i1){
+        save_to_sac(buffer, sacout_prefixes[0], chs[i1], arrsyn[i1], hdsyn[i1]);
+        if(calc_upar){
+            for(int i2=0; i2<3; ++i2){
+                save_to_sac(buffer, sacout_prefixes[i1+1], chs[i2], arrsyn_upar[i1][i2], hdsyn_upar[i1][i2]);
+            }
+        }
+    }
+
 
     // 保存时间函数
     if(tfnt > 0){
@@ -784,10 +824,9 @@ int main(int argc, char **argv){
             tfarr[i] *= fac;
             fac *= dfac;
         }
-        save_tf_to_sac(buffer, tfarr, tfnt, hd.delta);
+        save_tf_to_sac(buffer, tfarr, tfnt, dt);
     }  
 
-    free(arrout);
     free(buffer);
 
 
diff --git a/pygrt/C_extension/src/dynamic/propagate.c b/pygrt/C_extension/src/dynamic/propagate.c
index 3a9ab8f4..d98525c9 100755
--- a/pygrt/C_extension/src/dynamic/propagate.c
+++ b/pygrt/C_extension/src/dynamic/propagate.c
@@ -18,7 +18,7 @@
 #include "dynamic/propagate.h"
 #include "dynamic/layer.h"
 #include "dynamic/source.h"
-#include "common/bessel.h"
+#include "common/recursion.h"
 #include "common/model.h"
 #include "common/matrix.h"
 #include "common/prtdbg.h"
@@ -493,372 +493,3 @@ void kernel(
 
 }
 
-
-
-void int_Pk(
-    MYREAL k, MYREAL r, 
-    // F(ki,w)， 第一个维度3代表阶数m=0,1,2，第二个维度3代表三类系数qm,wm,vm 
-    const MYCOMPLEX EXP_qwv[3][3], const MYCOMPLEX VF_qwv[3][3], 
-    const MYCOMPLEX HF_qwv[3][3],  const MYCOMPLEX DC_qwv[3][3], 
-    // F(ki,w)Jm(ki*r)ki，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
-    bool calc_uir,
-    MYCOMPLEX EXP_J[3][4], MYCOMPLEX VF_J[3][4], 
-    MYCOMPLEX HF_J[3][4],  MYCOMPLEX DC_J[3][4])
-{
-    MYREAL bj0k, bj1k, bj2k;
-    MYREAL kr = k*r;
-    MYREAL kr_inv = RONE/kr;
-    MYREAL kcoef = k;
-
-    MYREAL J1coef, J2coef;
-
-    bessel012(kr, &bj0k, &bj1k, &bj2k); 
-    if(calc_uir){
-        MYREAL j1, j2;
-        j1 = bj1k;
-        j2 = bj2k;
-        besselp012(kr, &bj0k, &bj1k, &bj2k); 
-        kcoef = k*k;
-
-        J1coef = kr_inv * (-kr_inv * j1 + bj1k);
-        J2coef = kr_inv * (-kr_inv * j2 + bj2k);
-    } else {
-        J1coef = bj1k*kr_inv;
-        J2coef = bj2k*kr_inv;
-    }
-
-    J1coef *= kcoef;
-    J2coef *= kcoef;
-
-    bj0k *= kcoef;
-    bj1k *= kcoef;
-    bj2k *= kcoef;
-
-    
-    if(EXP_qwv!=NULL){
-    // 公式(5.6.22), 将公式分解为F(k,w)Jm(kr)k的形式
-    // m=0 爆炸源
-    EXP_J[0][0] = - EXP_qwv[0][0]*bj1k;
-    EXP_J[0][2] =   EXP_qwv[0][1]*bj0k;
-    }
-
-    if(VF_qwv!=NULL){
-    // m=0 垂直力源
-    VF_J[0][0] = - VF_qwv[0][0]*bj1k;
-    VF_J[0][2] =   VF_qwv[0][1]*bj0k;
-    }
-
-    if(HF_qwv!=NULL){
-    // m=1 水平力源
-    HF_J[1][0]  =   HF_qwv[1][0]*bj0k;         // q1*J0*k
-    HF_J[1][1]  = - (HF_qwv[1][0] + HF_qwv[1][2])*J1coef;    // - (q1+v1)*J1*k/kr
-    HF_J[1][2]  =   HF_qwv[1][1]*bj1k;         // w1*J1*k
-    HF_J[1][3]  = - HF_qwv[1][2]*bj0k;         // -v1*J0*k
-    }
-
-    if(DC_qwv!=NULL){
-    // m=0 双力偶源
-    DC_J[0][0] = - DC_qwv[0][0]*bj1k;
-    DC_J[0][2] =   DC_qwv[0][1]*bj0k;
-
-    // m=1 双力偶源
-    DC_J[1][0]  =   DC_qwv[1][0]*bj0k;         // q1*J0*k
-    DC_J[1][1]  = - (DC_qwv[1][0] + DC_qwv[1][2])*J1coef;    // - (q1+v1)*J1*k/kr
-    DC_J[1][2]  =   DC_qwv[1][1]*bj1k;         // w1*J1*k
-    DC_J[1][3]  = - DC_qwv[1][2]*bj0k;         // -v1*J0*k
-
-    // m=2 双力偶源
-    DC_J[2][0]  =   DC_qwv[2][0]*bj1k;         // q2*J1*k
-    DC_J[2][1]  = - RTWO*(DC_qwv[2][0] + DC_qwv[2][2])*J2coef;    // - (q2+v2)*J2*k/kr
-    DC_J[2][2]  =   DC_qwv[2][1]*bj2k;         // w2*J2*k
-    DC_J[2][3]  = - DC_qwv[2][2]*bj1k;         // -v2*J1*k
-    }
-}
-
-
-
-void merge_Pk(
-    // F(ki,w)Jm(ki*r)ki，维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型
-    const MYCOMPLEX sum_EXP_J[3][4], const MYCOMPLEX sum_VF_J[3][4], 
-    const MYCOMPLEX sum_HF_J[3][4],  const MYCOMPLEX sum_DC_J[3][4], 
-    // 累积求和，维度2代表Z、R分量，维度3代表Z、R、T分量 
-    MYCOMPLEX tol_EXP[2], MYCOMPLEX tol_VF[2], MYCOMPLEX tol_HF[3],
-    MYCOMPLEX tol_DD[2],  MYCOMPLEX tol_DS[3], MYCOMPLEX tol_SS[3])
-{   
-    if(sum_EXP_J!=NULL){
-    tol_EXP[0] = sum_EXP_J[0][2];
-    tol_EXP[1] = sum_EXP_J[0][0];
-    }
-
-    if(sum_VF_J!=NULL){
-    tol_VF[0] = sum_VF_J[0][2];
-    tol_VF[1] = sum_VF_J[0][0];
-    }
-
-    if(sum_HF_J!=NULL){
-    tol_HF[0] = sum_HF_J[1][2];
-    tol_HF[1] = sum_HF_J[1][0] + sum_HF_J[1][1];
-    tol_HF[2] = - sum_HF_J[1][1] + sum_HF_J[1][3];
-    }
-
-    if(sum_DC_J!=NULL){
-    tol_DD[0] = sum_DC_J[0][2];
-    tol_DD[1] = sum_DC_J[0][0];
-    
-    tol_DS[0] = sum_DC_J[1][2];
-    tol_DS[1] = sum_DC_J[1][0] + sum_DC_J[1][1];
-    tol_DS[2] = - sum_DC_J[1][1] + sum_DC_J[1][3];
-
-    tol_SS[0] = sum_DC_J[2][2];
-    tol_SS[1] = sum_DC_J[2][0] + sum_DC_J[2][1];
-    tol_SS[2] = - sum_DC_J[2][1] + sum_DC_J[2][3];
-    }
-}
-
-
-
-
-void recursion_RD(
-    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
-    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
-{
-    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
-
-    // RD, RDL
-    cmat2x2_mul(RU1, RD2, tmp1);
-    cmat2x2_one_sub(tmp1);
-    cmat2x2_inv(tmp1, tmp1);
-    cmat2x2_mul(tmp1, TD1, tmp2);
-    if(inv_2x2T!=NULL) cmat2x2_assign(tmp2, inv_2x2T);
-
-    cmat2x2_mul(RD2, tmp2, tmp1);
-    cmat2x2_mul(TU1, tmp1, tmp2);
-    cmat2x2_add(RD1, tmp2, RD);
-    inv1 = RONE / (RONE - RUL1*RDL2) * TDL1;
-    *RDL = RDL1 + TUL1*RDL2*inv1;
-    if(invT!=NULL)  *invT = inv1;
-}
-
-
-void recursion_TD(
-    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, 
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, 
-    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, 
-    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
-{
-    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
-
-    // TD, TDL
-    cmat2x2_mul(RU1, RD2, tmp2);
-    cmat2x2_one_sub(tmp2);
-    cmat2x2_inv(tmp2, tmp1);
-    cmat2x2_mul(tmp1, TD1, tmp2);
-    if(inv_2x2T!=NULL)  cmat2x2_assign(tmp2, inv_2x2T);
-    cmat2x2_mul(TD2, tmp2, TD);
-    
-    inv1 = RONE / (RONE - RUL1*RDL2) * TDL1;
-    *TDL = TDL2 * inv1;
-
-    if(invT!=NULL) *invT = inv1;
-}
-
-
-void recursion_RU(
-    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
-    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
-    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
-{
-    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
-
-    // RU, RUL
-    cmat2x2_mul(RD2, RU1, tmp2);
-    cmat2x2_one_sub(tmp2);
-    cmat2x2_inv(tmp2, tmp1);
-    cmat2x2_mul(tmp1, TU2, tmp2);
-    if(inv_2x2T!=NULL)  cmat2x2_assign(tmp2, inv_2x2T);
-
-    cmat2x2_mul(RU1, tmp2, tmp1); 
-    cmat2x2_mul(TD2, tmp1, tmp2);
-    cmat2x2_add(RU2, tmp2, RU);
-    inv1 = RONE / (RONE - RUL1*RDL2) * TUL2;
-    *RUL = RUL2 + TDL2*RUL1*inv1; 
-
-    if(invT!=NULL)  *invT = inv1;
-}
-
-
-void recursion_TU(
-    const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2,
-    const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
-    MYCOMPLEX TU[2][2], MYCOMPLEX *TUL, MYCOMPLEX inv_2x2T[2][2], MYCOMPLEX *invT)
-{
-    MYCOMPLEX tmp1[2][2], tmp2[2][2], inv1;
-
-    // TU, TUL
-    cmat2x2_mul(RD2, RU1, tmp2);
-    cmat2x2_one_sub(tmp2);
-    cmat2x2_inv(tmp2, tmp1);
-    cmat2x2_mul(tmp1, TU2, tmp2);
-    if(inv_2x2T!=NULL) cmat2x2_assign(tmp2, inv_2x2T);
-    cmat2x2_mul(TU1, tmp2, TU);
-    
-    inv1 = RONE / (RONE - RUL1*RDL2) * TUL2;
-    *TUL = TUL1 * inv1;
-
-    if(invT!=NULL)  *invT = inv1;
-
-}
-
-
-
-
-void recursion_RT_2x2(
-    const MYCOMPLEX RD1[2][2], MYCOMPLEX RDL1, const MYCOMPLEX RU1[2][2], MYCOMPLEX RUL1,
-    const MYCOMPLEX TD1[2][2], MYCOMPLEX TDL1, const MYCOMPLEX TU1[2][2], MYCOMPLEX TUL1,
-    const MYCOMPLEX RD2[2][2], MYCOMPLEX RDL2, const MYCOMPLEX RU2[2][2], MYCOMPLEX RUL2,
-    const MYCOMPLEX TD2[2][2], MYCOMPLEX TDL2, const MYCOMPLEX TU2[2][2], MYCOMPLEX TUL2,
-    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX RU[2][2], MYCOMPLEX *RUL,
-    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL)
-{
-
-    // 临时矩阵
-    MYCOMPLEX tmp1[2][2], tmp2[2][2];
-    MYCOMPLEX inv0, inv1T;
-
-    inv0 = RONE / (RONE - RUL1*RDL2);
-    // return;
-
-    // Rayleigh RD,TD
-    if( RD!=NULL || TD!=NULL ){
-        cmat2x2_mul(RU1, RD2, tmp1);
-        cmat2x2_one_sub(tmp1);
-        cmat2x2_inv(tmp1, tmp1);
-        cmat2x2_mul(tmp1, TD1, tmp2);
-
-        // TD
-        if(TD!=NULL){
-            cmat2x2_mul(TD2, tmp2, TD); // 相同的逆阵，节省计算量
-        }
-
-        // RD
-        if(RD!=NULL){
-            cmat2x2_mul(RD2, tmp2, tmp1);
-            cmat2x2_mul(TU1, tmp1, tmp2);
-            cmat2x2_add(RD1, tmp2, RD);
-        }
-    }
-
-    // Rayleigh RU,TU
-    if( RU!=NULL || TU!=NULL ){
-        cmat2x2_mul(RD2, RU1, tmp1);
-        cmat2x2_one_sub(tmp1);
-        cmat2x2_inv(tmp1, tmp1);
-        cmat2x2_mul(tmp1, TU2, tmp2);
-
-        // TU
-        if(TU!=NULL){
-            cmat2x2_mul(TU1, tmp2, TU);
-        }
-
-        // RU
-        if(RU!=NULL){
-            cmat2x2_mul(RU1, tmp2, tmp1);
-            cmat2x2_mul(TD2, tmp1, tmp2);
-            cmat2x2_add(RU2, tmp2, RU);
-        }
-    }
-
-
-    // Love RDL,TDL
-    if(RDL!=NULL || TDL!=NULL){
-        inv1T = inv0 * TDL1;
-        // TDL
-        if(TDL!=NULL){
-            *TDL = TDL2 * inv1T;
-        }
-        // RDL
-        if(RDL!=NULL){
-            *RDL = RDL1 + TUL1*RDL2*inv1T;
-        }
-    }
-
-    // Love RUL,TUL
-    if(RUL!=NULL || TUL!=NULL){
-        inv1T = inv0 * TUL2;
-        // TUL
-        if(TUL!=NULL){
-            *TUL = TUL1 * inv1T;
-        }
-
-        // RUL
-        if(RUL!=NULL){
-            *RUL = RUL2 + TDL2*RUL1 *inv1T; 
-        }
-    }
-
-}
-
-
-void recursion_RT_2x2_imaginary(
-    MYCOMPLEX xa1, MYCOMPLEX xb1, MYREAL thk, MYREAL k, // 使用上层的厚度
-    MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, 
-    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL)
-{
-    MYCOMPLEX exa, exb, exab, ex2a, ex2b; 
-    exa = CEXP(-k*thk*xa1);
-    exb = CEXP(-k*thk*xb1);
-
-    exab = exa * exb;
-    ex2a = exa * exa;
-    ex2b = exb * exb;
-
-
-    // 虚拟层位不是介质物理间断面
-    RU[0][0] *= ex2a;    RU[0][1] *= exab;  
-    RU[1][0] *= exab;    RU[1][1] *= ex2b;  
-    
-    TD[0][0] *= exa;     TD[0][1] *= exa; 
-    TD[1][0] *= exb;     TD[1][1] *= exb;
-
-    TU[0][0] *= exa;     TU[0][1] *= exb; 
-    TU[1][0] *= exa;     TU[1][1] *= exb;
-
-    *RUL *= ex2b;
-    *TDL *= exb;
-    *TUL *= exb;
-}
-
-
-
-
-void get_qwv(
-    bool ircvup, 
-    const MYCOMPLEX R1[2][2], MYCOMPLEX RL1, 
-    const MYCOMPLEX R2[2][2], MYCOMPLEX RL2, 
-    const MYCOMPLEX coef[3][2], MYCOMPLEX qwv[3])
-{
-    MYCOMPLEX qw0[2], qw1[2], v0;
-    MYCOMPLEX coefD[2] = {coef[0][0], coef[1][0]};
-    MYCOMPLEX coefU[2] = {coef[0][1], coef[1][1]};
-    if(ircvup){
-        cmat2x1_mul(R2, coefD, qw0);
-        qw0[0] += coefU[0]; qw0[1] += coefU[1]; 
-        v0 = RL1 * (RL2*coef[2][0] + coef[2][1]);
-    } else {
-        cmat2x1_mul(R2, coefU, qw0);
-        qw0[0] += coefD[0]; qw0[1] += coefD[1]; 
-        v0 = RL1 * (coef[2][0] + RL2*coef[2][1]);
-    }
-    cmat2x1_mul(R1, qw0, qw1);
-
-    qwv[0] = qw1[0];
-    qwv[1] = qw1[1];
-    qwv[2] = v0;
-}
-
diff --git a/pygrt/C_extension/src/static/Makefile b/pygrt/C_extension/src/static/Makefile
new file mode 100644
index 00000000..e0299d4e
--- /dev/null
+++ b/pygrt/C_extension/src/static/Makefile
@@ -0,0 +1,71 @@
+
+CC := gcc 
+CFLAGS := -Wall -g -fPIC -I../../include -lm
+
+COMMON_OBJS := $(wildcard ../../build/common/*.o)
+
+BUILD_DIR = ../../build/dynamic
+BIN_DIR = ../../bin
+SRCS := $(wildcard *.c)
+OBJS := $(patsubst %.c, $(BUILD_DIR)/%.o, $(SRCS))
+
+DEPS := $(OBJS:.o=.d)  # include main functions here
+
+OBJS := $(filter-out \
+	$(BUILD_DIR)/stgrt_main.o \
+	$(BUILD_DIR)/stgrt_syn.o \
+	$(BUILD_DIR)/stgrt_strain.o \
+	$(BUILD_DIR)/stgrt_stress.o \
+	, $(OBJS))
+
+PROGS := $(BIN_DIR)/stgrt \
+		 $(BIN_DIR)/stgrt.syn \
+		 $(BIN_DIR)/stgrt.strain \
+		 $(BIN_DIR)/stgrt.stress
+
+all: objs progs
+
+objs: $(BUILD_DIR) $(OBJS)
+
+progs: $(OBJS) $(BIN_DIR) $(PROGS)
+
+
+$(BUILD_DIR):
+	@mkdir -p $(BUILD_DIR)
+
+$(BUILD_DIR)/%.o: %.c
+	$(CC) -o $@  -c $< $(CFLAGS) 
+
+
+# ------------------------Executable files------------------------
+$(BIN_DIR):
+	@mkdir -p $(BIN_DIR)
+
+$(BIN_DIR)/stgrt: stgrt_main.c $(OBJS) $(COMMON_OBJS)
+	$(CC) -o $@ $^ $(CFLAGS)
+
+$(BIN_DIR)/stgrt.syn: stgrt_syn.c $(OBJS) $(COMMON_OBJS)
+	$(CC) -o $@ $^ $(CFLAGS)
+
+$(BIN_DIR)/stgrt.strain: stgrt_strain.c $(COMMON_OBJS) 
+	$(CC) -o $@ $^ $(CFLAGS)
+
+$(BIN_DIR)/stgrt.stress: stgrt_stress.c $(COMMON_OBJS) 
+	$(CC) -o $@ $^ $(CFLAGS)
+
+# ----------------------- Dependency generation -----------------------
+-include $(DEPS)
+
+$(BUILD_DIR)/%.d: %.c
+	@mkdir -p $(shell dirname $@)
+	@$(CC) $(CFLAGS) -MM $< > $@.$$$$; \
+	sed 's,\($*\)\.o[ :]*,$(BUILD_DIR)/\1.o $@ : ,g' < $@.$$$$ > $@; \
+	rm -f $@.$$$$
+
+# ---------------------------------------------------------------------
+
+cleanbuild:
+	rm -rf $(BUILD_DIR)
+
+clean: cleanbuild
+	rm -f $(PROGS)
\ No newline at end of file
diff --git a/pygrt/C_extension/src/static/static_layer.c b/pygrt/C_extension/src/static/static_layer.c
new file mode 100644
index 00000000..e2325819
--- /dev/null
+++ b/pygrt/C_extension/src/static/static_layer.c
@@ -0,0 +1,130 @@
+/**
+ * @file   static_layer.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ * 以下代码实现的是 静态反射透射系数矩阵 ，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *         2. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *              透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ *
+ */
+
+#include <stdio.h>
+#include <complex.h>
+#include <stdbool.h>
+
+#include "static/static_layer.h"
+#include "common/model.h"
+#include "common/matrix.h"
+
+void calc_static_R_tilt(MYCOMPLEX delta1, MYCOMPLEX R_tilt[2][2]){
+    // 公式(6.3.12)
+    R_tilt[0][0] = R_tilt[1][1] = CZERO;
+    R_tilt[0][1] = -delta1;
+    R_tilt[1][0] = -RONE/delta1;
+}
+
+void calc_static_R_EV(
+    bool ircvup,
+    const MYCOMPLEX R[2][2], MYCOMPLEX RL, 
+    MYCOMPLEX R_EV[2][2], MYCOMPLEX *R_EVL)
+{
+    MYCOMPLEX D11[2][2] = {{RONE, -RONE}, {RONE, RONE}};
+    MYCOMPLEX D12[2][2] = {{RONE, -RONE}, {-RONE, -RONE}};
+
+    // 公式(6.3.35,37)
+    if(ircvup){// 震源更深
+        cmat2x2_mul(D12, R, R_EV);
+        cmat2x2_add(D11, R_EV, R_EV);
+    } else { // 接收点更深
+        cmat2x2_mul(D11, R, R_EV);
+        cmat2x2_add(D12, R_EV, R_EV);
+    }
+    *R_EVL = (RONE + (RL));
+}
+
+void calc_static_uiz_R_EV(
+    MYCOMPLEX delta1, bool ircvup, MYREAL k, 
+    const MYCOMPLEX R[2][2], MYCOMPLEX RL, 
+    MYCOMPLEX R_EV[2][2], MYCOMPLEX *R_EVL)
+{
+    // 新推导公式
+    MYCOMPLEX kd2 = RTWO*k*delta1;
+    MYCOMPLEX D11[2][2] = {{k, -k-kd2}, {k, k-kd2}};
+    MYCOMPLEX D12[2][2] = {{-k, k+kd2}, {k, k-kd2}};
+    if(ircvup){// 震源更深
+        cmat2x2_mul(D12, R, R_EV);
+        cmat2x2_add(D11, R_EV, R_EV);
+        *R_EVL = (RONE - (RL))*k;
+    } else { // 接收点更深
+        cmat2x2_mul(D11, R, R_EV);
+        cmat2x2_add(D12, R_EV, R_EV);
+        *R_EVL = (RL - RONE)*k;
+    }
+}
+
+
+void calc_static_RT_2x2(
+    MYCOMPLEX delta1, MYCOMPLEX mu1, 
+    MYCOMPLEX delta2, MYCOMPLEX mu2, 
+    MYREAL thk, MYREAL k,
+    MYCOMPLEX RD[2][2], MYCOMPLEX *RDL, MYCOMPLEX RU[2][2], MYCOMPLEX *RUL, 
+    MYCOMPLEX TD[2][2], MYCOMPLEX *TDL, MYCOMPLEX TU[2][2], MYCOMPLEX *TUL)
+{
+    // 公式(6.3.18)
+
+    MYCOMPLEX ex, ex2; 
+
+    ex = CEXP(-k*thk);
+    ex2 = ex*ex;
+
+    MYCOMPLEX dmu = mu1 - mu2;
+    MYCOMPLEX amu = mu1 + mu2;
+    MYCOMPLEX A112 = mu1*delta1 + mu2;
+    MYCOMPLEX A221 = mu2*delta2 + mu1;
+    MYCOMPLEX B = mu1*delta1 - mu2*delta2;
+    MYCOMPLEX del11 = delta1*delta1;
+    MYREAL k2 = k*k;
+    MYREAL thk2 = thk*thk;
+
+    // REFELCTION
+    //------------------ RD -----------------------------------
+    RD[0][0] = -RTWO*delta1*k*thk*dmu/A112 * ex2;
+    RD[0][1] = - ( RFOUR*del11*k2*thk2*A221*dmu + A112*B ) / (A221*A112) * ex2;
+    RD[1][0] = - dmu/A112 * ex2;
+    RD[1][1] = RD[0][0];
+    //------------------ RU -----------------------------------
+    RU[0][0] = RZERO;
+    RU[0][1] = B/A112;
+    RU[1][0] = dmu/A221;
+    RU[1][1] = RZERO;
+
+    *RDL = dmu/amu * ex2;
+    *RUL = - dmu/amu;
+
+    // Transmission
+    //------------------ TD -----------------------------------
+    TD[0][0] = mu1*(RONE+delta1)/(A112) * ex;
+    TD[0][1] = RTWO*mu1*delta1*k*thk*(RONE+delta1)/(A112) * ex;
+    TD[1][0] = RZERO;
+    TD[1][1] = TD[0][0]*A112/A221;
+    //------------------ TU -----------------------------------
+    TU[0][0] = mu2*(RONE+delta2)/A221 * ex;
+    TU[0][1] = RTWO*delta1*k*thk*mu2*(RONE+delta2)/A112 * ex;
+    TU[1][0] = RZERO;
+    TU[1][1] = TU[0][0]*A221/A112;
+
+    *TDL = RTWO*mu1/amu * ex;
+    *TUL = (*TDL)*mu2/mu1;
+
+    // printf("delta1=%.5e%+.5ej, delta2=%.5e%+.5ej, mu1=%.5e%+.5ej, mu2=%.5e%+.5ej, thk=%e, k=%e\n", 
+    //         CREAL(delta1),CIMAG(delta1),CREAL(delta2),CIMAG(delta2),CREAL(mu1),CIMAG(mu1),CREAL(mu2),CIMAG(mu2),
+    //         thk, k);
+    // cmatmxn_print(2, 2, RD);
+    // cmatmxn_print(2, 2, RU);
+    // cmatmxn_print(2, 2, TD);
+    // cmatmxn_print(2, 2, TU);
+    // printf("-----------------------------\n");
+}
\ No newline at end of file
diff --git a/pygrt/C_extension/src/static/static_propagate.c b/pygrt/C_extension/src/static/static_propagate.c
new file mode 100644
index 00000000..fc86f704
--- /dev/null
+++ b/pygrt/C_extension/src/static/static_propagate.c
@@ -0,0 +1,457 @@
+/**
+ * @file   static_propagate.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ * 以下代码实现的是 静态广义反射透射系数矩阵 ，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *         2. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *              透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ *
+ */
+
+
+#include <stdio.h>
+#include <complex.h>
+
+#include "static/static_propagate.h"
+#include "static/static_layer.h"
+#include "static/static_source.h"
+#include "common/recursion.h"
+#include "common/model.h"
+#include "common/const.h"
+#include "common/matrix.h"
+
+#define CMAT_ASSIGN_SPLIT 0  // 2x2的小矩阵赋值合并为1个循环，程序速度提升微小
+
+
+void static_kernel(
+    const MODEL1D *mod1d, MYCOMPLEX omega, MYREAL k,
+    MYCOMPLEX EXP_qwv[3][3], MYCOMPLEX VF_qwv[3][3], MYCOMPLEX HF_qwv[3][3], MYCOMPLEX DC_qwv[3][3],
+    bool calc_uiz,
+    MYCOMPLEX EXP_uiz_qwv[3][3], MYCOMPLEX VF_uiz_qwv[3][3], MYCOMPLEX HF_uiz_qwv[3][3], MYCOMPLEX DC_uiz_qwv[3][3])
+{
+    // 初始化qwv为0
+    for(MYINT i=0; i<3; ++i){
+        for(MYINT j=0; j<3; ++j){
+            if(EXP_qwv!=NULL) EXP_qwv[i][j] = RZERO;
+            if(VF_qwv!=NULL)  VF_qwv[i][j] = RZERO;
+            if(HF_qwv!=NULL)  HF_qwv[i][j] = RZERO;
+            if(DC_qwv!=NULL)  DC_qwv[i][j] = RZERO;
+        }
+    }
+    
+    if(calc_uiz){
+        // 初始化qwv为0
+        for(MYINT i=0; i<3; ++i){
+            for(MYINT j=0; j<3; ++j){
+                if(EXP_uiz_qwv!=NULL) EXP_uiz_qwv[i][j] = RZERO;
+                if(VF_uiz_qwv!=NULL)  VF_uiz_qwv[i][j] = RZERO;
+                if(HF_uiz_qwv!=NULL)  HF_uiz_qwv[i][j] = RZERO;
+                if(DC_uiz_qwv!=NULL)  DC_uiz_qwv[i][j] = RZERO;
+            }
+        }
+    }
+
+    bool ircvup = mod1d->ircvup;
+    MYINT isrc = mod1d->isrc; // 震源所在虚拟层位, isrc>=1
+    MYINT ircv = mod1d->ircv; // 接收点所在虚拟层位, ircv>=1, ircv != isrc
+    MYINT imin, imax; // 相对浅层深层层位
+    imin = mod1d->imin;
+    imax = mod1d->imax;
+
+    // 初始化广义反射透射系数矩阵
+    // BL
+    MYCOMPLEX RD_BL[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RDL_BL = CZERO;
+    MYCOMPLEX RU_BL[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RUL_BL = CZERO;
+    MYCOMPLEX TD_BL[2][2] = INIT_C_IDENTITY_2x2_MATRIX;
+    MYCOMPLEX TDL_BL = CONE;
+    MYCOMPLEX TU_BL[2][2] = INIT_C_IDENTITY_2x2_MATRIX;
+    MYCOMPLEX TUL_BL = CONE;
+    // AL
+    MYCOMPLEX RD_AL[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RDL_AL = CZERO;
+    // RS
+    MYCOMPLEX RD_RS[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RDL_RS = CZERO;
+    MYCOMPLEX RU_RS[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RUL_RS = CZERO;
+    MYCOMPLEX TD_RS[2][2] = INIT_C_IDENTITY_2x2_MATRIX;
+    MYCOMPLEX TDL_RS = CONE;
+    MYCOMPLEX TU_RS[2][2] = INIT_C_IDENTITY_2x2_MATRIX;
+    MYCOMPLEX TUL_RS = CONE;
+    // FA (实际先计算ZA，再递推到FA)
+    MYCOMPLEX RD_FA[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RDL_FA = CZERO;
+    MYCOMPLEX RU_FA[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RUL_FA = CZERO;
+    MYCOMPLEX TD_FA[2][2] = INIT_C_IDENTITY_2x2_MATRIX;
+    MYCOMPLEX TDL_FA = CONE;
+    MYCOMPLEX TU_FA[2][2] = INIT_C_IDENTITY_2x2_MATRIX;
+    MYCOMPLEX TUL_FA = CONE;
+    // FB (实际先计算ZB，再递推到FB)
+    MYCOMPLEX RU_FB[2][2] = INIT_C_ZERO_2x2_MATRIX;
+    MYCOMPLEX RUL_FB = CZERO;
+
+    // 抽象指针 
+    // BL
+    MYCOMPLEX *const pRDL_BL = &RDL_BL;
+    MYCOMPLEX *const pRUL_BL = &RUL_BL;
+    MYCOMPLEX *const pTDL_BL = &TDL_BL;
+    MYCOMPLEX *const pTUL_BL = &TUL_BL;
+    // AL
+    MYCOMPLEX *const pRDL_AL = &RDL_AL;
+    // RS
+    MYCOMPLEX *const pRDL_RS = &RDL_RS;
+    MYCOMPLEX *const pRUL_RS = &RUL_RS;
+    MYCOMPLEX *const pTDL_RS = &TDL_RS;
+    MYCOMPLEX *const pTUL_RS = &TUL_RS;
+    // FA
+    MYCOMPLEX *const pRDL_FA = &RDL_FA;
+    MYCOMPLEX *const pRUL_FA = &RUL_FA;
+    MYCOMPLEX *const pTDL_FA = &TDL_FA;
+    MYCOMPLEX *const pTUL_FA = &TUL_FA;
+    // FB 
+    MYCOMPLEX *const pRUL_FB = &RUL_FB;
+
+
+    // 定义物理层内的反射透射系数矩阵，相对于界面上的系数矩阵增加了时间延迟因子
+    MYCOMPLEX RD[2][2], RDL, TD[2][2], TDL;
+    MYCOMPLEX RU[2][2], RUL, TU[2][2], TUL;
+    MYCOMPLEX *const pRDL = &RDL;
+    MYCOMPLEX *const pTDL = &TDL;
+    MYCOMPLEX *const pRUL = &RUL;
+    MYCOMPLEX *const pTUL = &TUL;
+
+
+    // 自由表面的反射系数
+    MYCOMPLEX R_tilt[2][2] = INIT_C_ZERO_2x2_MATRIX; // SH波在自由表面的反射系数为1，不必定义变量
+
+    // 接收点处的接收矩阵
+    MYCOMPLEX R_EV[2][2], R_EVL;
+    MYCOMPLEX *const pR_EVL = &R_EVL;
+    
+    // 接收点处的接收矩阵(转为位移导数ui_z的(B_m, C_m, P_m)系分量)
+    MYCOMPLEX uiz_R_EV[2][2], uiz_R_EVL;
+    MYCOMPLEX *const puiz_R_EVL = &uiz_R_EVL;
+
+
+    // 模型参数
+    // 后缀0，1分别代表上层和下层
+    LAYER *lay = NULL;
+    MYREAL mod1d_thk0, mod1d_thk1;
+    MYCOMPLEX mod1d_mu0, mod1d_mu1;
+    MYCOMPLEX mod1d_delta0, mod1d_delta1;
+    MYCOMPLEX top_delta = CZERO;
+    MYCOMPLEX src_delta = CZERO;
+    MYCOMPLEX rcv_delta = CZERO;
+    
+
+    // 从顶到底进行矩阵递推, 公式(5.5.3)
+    for(MYINT iy=0; iy<mod1d->n; ++iy){ // 因为n>=3, 故一定会进入该循环
+        lay = mod1d->lays + iy;
+
+        // 赋值上层 
+        mod1d_thk0 = mod1d_thk1;
+        mod1d_mu0 = mod1d_mu1;
+        mod1d_delta0 = mod1d_delta1;
+
+        // 更新模型参数
+        mod1d_thk1 = lay->thk;
+        mod1d_mu1 = lay->mu;
+        mod1d_delta1 = lay->delta;
+
+        if(0==iy){
+            top_delta = mod1d_delta1;
+            continue;
+        }
+
+        // 确定上下层的物性参数
+        if(ircv==iy){
+            rcv_delta = mod1d_delta1;
+        } else if(isrc==iy){
+            src_delta = mod1d_delta1;
+        }
+
+        // 对第iy层的系数矩阵赋值，加入时间延迟因子(第iy-1界面与第iy界面之间)
+        calc_static_RT_2x2(
+            mod1d_delta0, mod1d_mu0,
+            mod1d_delta1, mod1d_mu1,
+            mod1d_thk0, k, // 使用iy-1层的厚度
+            RD, pRDL, RU, pRUL, 
+            TD, pTDL, TU, pTUL);
+
+        // FA
+        if(iy <= imin){
+            if(iy == 1){ // 初始化FA
+#if CMAT_ASSIGN_SPLIT == 1
+                cmat2x2_assign(RD, RD_FA);  RDL_FA = RDL;
+                cmat2x2_assign(RU, RU_FA);  RUL_FA = RUL;
+                cmat2x2_assign(TD, TD_FA);  TDL_FA = TDL;
+                cmat2x2_assign(TU, TU_FA);  TUL_FA = TUL;
+#else 
+                for(MYINT kk=0; kk<2; ++kk){
+                    for(MYINT pp=0; pp<2; ++pp){
+                        RD_FA[kk][pp] = RD[kk][pp];
+                        RU_FA[kk][pp] = RU[kk][pp];
+                        TD_FA[kk][pp] = TD[kk][pp];
+                        TU_FA[kk][pp] = TU[kk][pp];
+                    }
+                }
+                RDL_FA = RDL;
+                RUL_FA = RUL;
+                TDL_FA = TDL;
+                TUL_FA = TUL;
+#endif
+            } else { // 递推FA
+
+                recursion_RT_2x2(
+                    RD_FA, RDL_FA, RU_FA, RUL_FA, 
+                    TD_FA, TDL_FA, TU_FA, TUL_FA,
+                    RD, RDL, RU, RUL, 
+                    TD, TDL, TU, TUL,
+                    RD_FA, pRDL_FA, RU_FA, pRUL_FA, 
+                    TD_FA, pTDL_FA, TU_FA, pTUL_FA);  
+            }
+
+        }
+        // RS
+        else if(iy <= imax){
+            if(iy == imin+1){// 初始化RS
+#if CMAT_ASSIGN_SPLIT == 1
+                cmat2x2_assign(RD, RD_RS);  RDL_RS = RDL;
+                cmat2x2_assign(RU, RU_RS);  RUL_RS = RUL;
+                cmat2x2_assign(TD, TD_RS);  TDL_RS = TDL;
+                cmat2x2_assign(TU, TU_RS);  TUL_RS = TUL;
+#else
+                for(MYINT kk=0; kk<2; ++kk){
+                    for(MYINT pp=0; pp<2; ++pp){
+                        RD_RS[kk][pp] = RD[kk][pp];
+                        RU_RS[kk][pp] = RU[kk][pp];
+                        TD_RS[kk][pp] = TD[kk][pp];
+                        TU_RS[kk][pp] = TU[kk][pp];
+                    }
+                }
+                RDL_RS = RDL;
+                RUL_RS = RUL;
+                TDL_RS = TDL;
+                TUL_RS = TUL;
+#endif
+            } else { // 递推RS
+                recursion_RT_2x2(
+                    RD_RS, RDL_RS, RU_RS, RUL_RS, 
+                    TD_RS, TDL_RS, TU_RS, TUL_RS,
+                    RD, RDL, RU, RUL, 
+                    TD, TDL, TU, TUL,
+                    RD_RS, pRDL_RS, RU_RS, pRUL_RS, 
+                    TD_RS, pTDL_RS, TU_RS, pTUL_RS);  // 写入原地址
+            }
+        } 
+        // BL
+        else {
+            if(iy == imax+1){// 初始化BL
+#if CMAT_ASSIGN_SPLIT == 1
+                cmat2x2_assign(RD, RD_BL);  RDL_BL = RDL;
+                cmat2x2_assign(RU, RU_BL);  RUL_BL = RUL;
+                cmat2x2_assign(TD, TD_BL);  TDL_BL = TDL;
+                cmat2x2_assign(TU, TU_BL);  TUL_BL = TUL;
+#else 
+                for(MYINT kk=0; kk<2; ++kk){
+                    for(MYINT pp=0; pp<2; ++pp){
+                        RD_BL[kk][pp] = RD[kk][pp];
+                        RU_BL[kk][pp] = RU[kk][pp];
+                        TD_BL[kk][pp] = TD[kk][pp];
+                        TU_BL[kk][pp] = TU[kk][pp];
+                    }
+                }
+                RDL_BL = RDL;
+                RUL_BL = RUL;
+                TDL_BL = TDL;
+                TUL_BL = TUL;
+#endif
+            } else { // 递推BL
+
+                // 这个IF纯粹是为了优化，因为不论是SL还是RL，只有RD矩阵最终会被使用到
+                // 这里最终只把RD矩阵的值记录下来，其它的舍去，以减少部分运算
+                if(iy < mod1d->n - 1){
+                    recursion_RT_2x2(
+                        RD_BL, RDL_BL, RU_BL, RUL_BL, 
+                        TD_BL, TDL_BL, TU_BL, TUL_BL,
+                        RD, RDL, RU, RUL, 
+                        TD, TDL, TU, TUL,
+                        RD_BL, pRDL_BL, RU_BL, pRUL_BL, 
+                        TD_BL, pTDL_BL, TU_BL, pTUL_BL);  // 写入原地址
+                } else {
+                    recursion_RT_2x2(
+                        RD_BL, RDL_BL, RU_BL, RUL_BL, 
+                        TD_BL, TDL_BL, TU_BL, TUL_BL,
+                        RD, RDL, RU, RUL, 
+                        TD, TDL, TU, TUL,
+                        RD_BL, pRDL_BL, NULL, NULL, 
+                        NULL, NULL, NULL, NULL);  // 写入原地址
+                }
+                
+            }
+
+        } // END if
+
+    } // END for loop
+    //===================================================================================
+
+
+    // 计算震源系数
+    MYCOMPLEX EXP[3][3][2], VF[3][3][2], HF[3][3][2], DC[3][3][2];
+    MYCOMPLEX (*pEXP)[3][2] = (EXP_qwv!=NULL)? EXP : NULL; 
+    MYCOMPLEX (*pVF)[3][2]  = (VF_qwv!=NULL)?  VF  : NULL; 
+    MYCOMPLEX (*pHF)[3][2]  = (HF_qwv!=NULL)?  HF  : NULL; 
+    MYCOMPLEX (*pDC)[3][2]  = (DC_qwv!=NULL)?  DC  : NULL; 
+    for(MYINT i=0; i<3; ++i){
+        for(MYINT j=0; j<3; ++j){
+            for(MYINT p=0; p<2; ++p){
+                EXP[i][j][p] = VF[i][j][p] = HF[i][j][p] = DC[i][j][p] = RZERO;
+            }
+        }
+    }
+    static_source_coef(src_delta, k, pEXP, pVF, pHF, pDC);
+
+    // 临时中转矩阵 (temperary)
+    MYCOMPLEX tmpR1[2][2], tmpR2[2][2], tmp2x2[2][2], tmpRL, tmp2x2_uiz[2][2], tmpRL_uiz;
+    MYCOMPLEX inv_2x2T[2][2], invT;
+
+    // 递推RU_FA
+    calc_static_R_tilt(top_delta, R_tilt);
+    recursion_RU(
+        R_tilt, RONE, 
+        RD_FA, RDL_FA,
+        RU_FA, RUL_FA, 
+        TD_FA, TDL_FA,
+        TU_FA, TUL_FA,
+        RU_FA, pRUL_FA, NULL, NULL);
+
+    // 根据震源和台站相对位置，计算最终的系数
+    if(ircvup){ // A接收  B震源
+        // 计算R_EV
+        calc_static_R_EV(ircvup, RU_FA, RUL_FA, R_EV, pR_EVL);
+
+        // 递推RU_FS
+        recursion_RU(
+            RU_FA, RUL_FA, // 已从ZR变为FR，加入了自由表面的效应
+            RD_RS, RDL_RS,
+            RU_RS, RUL_RS, 
+            TD_RS, TDL_RS,
+            TU_RS, TUL_RS,
+            RU_FB, pRUL_FB, inv_2x2T, &invT);
+        
+        // 公式(5.7.12-14)
+        // cmat2x2_mul(R_EV, inv_2x2T, tmpR1);
+        cmat2x2_mul(RD_BL, RU_FB, tmpR2);
+        cmat2x2_one_sub(tmpR2);
+        cmat2x2_inv(tmpR2, tmpR2);// (I - xx)^-1
+        cmat2x2_mul(inv_2x2T, tmpR2, tmp2x2);
+
+        if(calc_uiz) cmat2x2_assign(tmp2x2, tmp2x2_uiz); // 为后续计算空间导数备份
+
+        cmat2x2_mul(R_EV, tmp2x2, tmp2x2);
+        tmpRL = R_EVL * invT  / (RONE - RDL_BL * RUL_FB);
+        for(MYINT m=0; m<3; ++m){
+            if(0==m){
+                // 爆炸源
+                if(EXP_qwv!=NULL) get_qwv(ircvup, tmp2x2, tmpRL, RD_BL, RDL_BL, EXP[m], EXP_qwv[m]);
+                // 垂直力源
+                if(VF_qwv!=NULL)  get_qwv(ircvup, tmp2x2, tmpRL, RD_BL, RDL_BL, VF[m], VF_qwv[m]);
+            }
+            
+            // 水平力源
+            if(1==m && HF_qwv!=NULL) get_qwv(ircvup, tmp2x2, tmpRL, RD_BL, RDL_BL, HF[m], HF_qwv[m]);
+
+            // 剪切位错
+            if(DC_qwv!=NULL)  get_qwv(ircvup, tmp2x2, tmpRL, RD_BL, RDL_BL, DC[m], DC_qwv[m]);
+        }
+        
+
+        if(calc_uiz){
+            calc_static_uiz_R_EV(rcv_delta, ircvup, k, RU_FA, RUL_FA, uiz_R_EV, puiz_R_EVL);
+            cmat2x2_mul(uiz_R_EV, tmp2x2_uiz, tmp2x2_uiz);
+            tmpRL_uiz = tmpRL / R_EVL * uiz_R_EVL;
+            for(MYINT m=0; m<3; ++m){
+                if(0==m){
+                    // 爆炸源
+                    if(EXP_uiz_qwv!=NULL) get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RD_BL, RDL_BL, EXP[m], EXP_uiz_qwv[m]);
+                    // 垂直力源
+                    if(VF_uiz_qwv!=NULL)  get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RD_BL, RDL_BL, VF[m], VF_uiz_qwv[m]);
+                }
+                
+                // 水平力源
+                if(1==m && HF_uiz_qwv!=NULL) get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RD_BL, RDL_BL, HF[m], HF_uiz_qwv[m]);
+
+                // 剪切位错
+                if(DC_uiz_qwv!=NULL)  get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RD_BL, RDL_BL, DC[m], DC_uiz_qwv[m]);
+            }    
+        }
+    }
+    else { // A震源  B接收
+
+        // 计算R_EV
+        calc_static_R_EV(ircvup, RD_BL, RDL_BL, R_EV, pR_EVL);    
+
+        // 递推RD_SL
+        recursion_RD(
+            RD_RS, RDL_RS,
+            RU_RS, RUL_RS,
+            TD_RS, TDL_RS,
+            TU_RS, TUL_RS,
+            RD_BL, RDL_BL,
+            RD_AL, pRDL_AL, inv_2x2T, &invT);
+        
+        // 公式(5.7.26-27)
+        // cmat2x2_mul(R_EV, inv_2x2T, tmpR1);
+        cmat2x2_mul(RU_FA, RD_AL, tmpR2);
+        cmat2x2_one_sub(tmpR2);
+        cmat2x2_inv(tmpR2, tmpR2);// (I - xx)^-1
+        cmat2x2_mul(inv_2x2T, tmpR2, tmp2x2);
+        
+        if(calc_uiz) cmat2x2_assign(tmp2x2, tmp2x2_uiz); // 为后续计算空间导数备份
+
+        cmat2x2_mul(R_EV, tmp2x2, tmp2x2);
+        tmpRL = R_EVL * invT / (RONE - RUL_FA * RDL_AL);
+        for(MYINT m=0; m<3; ++m){
+            if(0==m){
+                // 爆炸源
+                if(EXP_qwv!=NULL) get_qwv(ircvup, tmp2x2, tmpRL, RU_FA, RUL_FA, EXP[m], EXP_qwv[m]);
+                // 垂直力源
+                if(VF_qwv!=NULL)  get_qwv(ircvup, tmp2x2, tmpRL, RU_FA, RUL_FA, VF[m], VF_qwv[m]);
+            }
+            
+            // 水平力源
+            if(1==m && HF_qwv!=NULL) get_qwv(ircvup, tmp2x2, tmpRL, RU_FA, RUL_FA, HF[m], HF_qwv[m]);
+
+            // 剪切位错
+            if(DC_qwv!=NULL)  get_qwv(ircvup, tmp2x2, tmpRL, RU_FA, RUL_FA, DC[m], DC_qwv[m]);
+
+        }
+
+        if(calc_uiz){
+            calc_static_uiz_R_EV(rcv_delta, ircvup, k, RD_BL, RDL_BL, uiz_R_EV, puiz_R_EVL);    
+            cmat2x2_mul(uiz_R_EV, tmp2x2_uiz, tmp2x2_uiz);
+            tmpRL_uiz = tmpRL / R_EVL * uiz_R_EVL;
+            for(MYINT m=0; m<3; ++m){
+                if(0==m){
+                    // 爆炸源
+                    if(EXP_uiz_qwv!=NULL) get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RU_FA, RUL_FA, EXP[m], EXP_uiz_qwv[m]);
+                    // 垂直力源
+                    if(VF_uiz_qwv!=NULL)  get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RU_FA, RUL_FA, VF[m], VF_uiz_qwv[m]);
+                }
+                
+                // 水平力源
+                if(1==m && HF_uiz_qwv!=NULL) get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RU_FA, RUL_FA, HF[m], HF_uiz_qwv[m]);
+    
+                // 剪切位错
+                if(DC_uiz_qwv!=NULL)  get_qwv(ircvup, tmp2x2_uiz, tmpRL_uiz, RU_FA, RUL_FA, DC[m], DC_uiz_qwv[m]);
+    
+            }
+        }
+    } // END if
+}
diff --git a/pygrt/C_extension/src/static/static_source.c b/pygrt/C_extension/src/static/static_source.c
new file mode 100644
index 00000000..c6c3f191
--- /dev/null
+++ b/pygrt/C_extension/src/static/static_source.c
@@ -0,0 +1,70 @@
+/**
+ * @file   static_source.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ * 以下代码实现的是 静态震源系数————剪切源， 参考：
+ *             1. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *                透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ *
+ */
+
+
+#include <stdio.h>
+#include <complex.h>
+
+#include "static/static_source.h"
+#include "common/const.h"
+
+
+void static_source_coef(
+    MYCOMPLEX delta, MYREAL k,
+    MYCOMPLEX EXP[3][3][2], MYCOMPLEX VF[3][3][2], MYCOMPLEX HF[3][3][2], MYCOMPLEX DC[3][3][2])
+{
+    // 先全部赋0 
+    for(MYINT i=0; i<3; ++i){
+        for(MYINT j=0; j<3; ++j){
+            for(MYINT p=0; p<2; ++p){
+                if(EXP!=NULL) EXP[i][j][p] = RZERO;
+                if(VF!=NULL)  VF[i][j][p] = RZERO;
+                if(HF!=NULL)  HF[i][j][p] = RZERO;
+                if(DC!=NULL)  DC[i][j][p] = RZERO;
+            }
+        }
+    }
+
+    MYCOMPLEX tmp;
+    MYCOMPLEX A = RONE+delta;
+
+    if(EXP!=NULL){
+    EXP[0][0][0] = tmp = (delta-RONE)/A;         EXP[0][0][1] = tmp;    
+    }
+
+    if(VF!=NULL){
+    VF[0][0][0] = tmp = -RONE/(RTWO*A*k);        VF[0][0][1] = - tmp;   
+    VF[0][1][0] = tmp;                           VF[0][1][1] = - tmp;
+    }
+
+    if(HF!=NULL){
+    HF[1][0][0] = tmp = RONE/(RTWO*A*k);        HF[1][0][1] = tmp;   
+    HF[1][1][0] = - tmp;                        HF[1][1][1] = - tmp;
+    HF[1][2][0] = tmp = -RONE/k;                HF[1][2][1] = tmp;
+    }
+
+
+    if(DC!=NULL){
+    // m=0
+    DC[0][0][0] = tmp = (-RONE+RFOUR*delta)/(RTWO*A);    DC[0][0][1] = tmp;
+    DC[0][1][0] = tmp = -RTHREE/(RTWO*A);                DC[0][1][1] = tmp;
+    // m=1
+    DC[1][0][0] = tmp = -delta/A;                        DC[1][0][1] = -tmp;
+    DC[1][1][0] = tmp = RONE/A;                          DC[1][1][1] = -tmp;
+    DC[1][2][0] = tmp = RONE;                            DC[1][2][1] = -tmp;
+    // m=2
+    DC[2][0][0] = tmp = RONE/(RTWO*A);                   DC[2][0][1] = tmp;
+    DC[2][1][0] = tmp = -RONE/(RTWO*A);                  DC[2][1][1] = tmp;
+    DC[2][2][0] = tmp = -RONE;                           DC[2][2][1] = tmp;
+    }
+}
+
+
diff --git a/pygrt/C_extension/src/static/stgrt.c b/pygrt/C_extension/src/static/stgrt.c
new file mode 100644
index 00000000..fda48d9d
--- /dev/null
+++ b/pygrt/C_extension/src/static/stgrt.c
@@ -0,0 +1,325 @@
+/**
+ * @file   stgrt.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-03
+ * 
+ * 以下代码实现的是 广义反射透射系数矩阵+离散波数法 计算静态格林函数，参考：
+ * 
+ *         1. 姚振兴, 谢小碧. 2022/03. 理论地震图及其应用（初稿）.  
+ *         2. 谢小碧, 姚振兴, 1989. 计算分层介质中位错点源静态位移场的广义反射、
+ *              透射系数矩阵和离散波数方法[J]. 地球物理学报(3): 270-280.
+ * 
+ */
+
+
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include "static/stgrt.h"
+#include "static/static_propagate.h"
+#include "common/dwm.h"
+#include "common/ptam.h"
+#include "common/fim.h"
+#include "common/const.h"
+#include "common/model.h"
+#include "common/integral.h"
+#include "common/search.h"
+
+
+
+/**
+ * 将计算好的复数形式的积分结果取实部记录到浮点数中
+ * 
+ * @param    iw      (in)当前频率索引值
+ * @param    nr      (in)震中距个数
+ * @param    coef    (in)统一系数
+ * @param  sum_EXP_J[nr][3][4]  (in)爆炸源
+ * @param  sum_VF_J[nr][3][4]   (in)垂直力源
+ * @param  sum_HF_J[nr][3][4]   (in)水平力源
+ * @param  sum_DC_J[nr][3][4]   (in)双力偶源
+ * @param      EXPgrn[nr][2]      (out)浮点数数组，爆炸源的Z、R分量频谱结果
+ * @param      VFgrn[nr][2]       (out)浮点数数组，垂直力源的Z、R分量频谱结果
+ * @param      HFgrn[nr][3]       (out)浮点数数组，水平力源的Z、R、T分量频谱结果
+ * @param      DDgrn[nr][2]       (out)浮点数数组，45度倾滑的Z、R分量频谱结果
+ * @param      DSgrn[nr][3]       (out)浮点数数组，90度倾滑的Z、R、T分量频谱结果
+ * @param      SSgrn[nr][3]       (out)浮点数数组，90度走滑的Z、R、T分量频谱结果
+ */
+static void recordin_GRN(
+    MYINT nr, MYCOMPLEX coef, 
+    MYCOMPLEX sum_EXP_J[nr][3][4], MYCOMPLEX sum_VF_J[nr][3][4],  
+    MYCOMPLEX sum_HF_J[nr][3][4],  MYCOMPLEX sum_DC_J[nr][3][4],  
+    MYREAL EXPgrn[nr][2], MYREAL VFgrn[nr][2], MYREAL HFgrn[nr][3],
+    MYREAL DDgrn[nr][2],  MYREAL DSgrn[nr][3], MYREAL SSgrn[nr][3]
+){
+    // 局部变量，将某个频点的格林函数谱临时存放
+    MYCOMPLEX (*tmp_EXP)[2] = (MYCOMPLEX(*)[2])calloc(nr, sizeof(*tmp_EXP));
+    MYCOMPLEX (*tmp_VF)[2] = (MYCOMPLEX(*)[2])calloc(nr, sizeof(*tmp_VF));
+    MYCOMPLEX (*tmp_HF)[3] = (MYCOMPLEX(*)[3])calloc(nr, sizeof(*tmp_HF));
+    MYCOMPLEX (*tmp_DD)[2] = (MYCOMPLEX(*)[2])calloc(nr, sizeof(*tmp_DD));
+    MYCOMPLEX (*tmp_DS)[3] = (MYCOMPLEX(*)[3])calloc(nr, sizeof(*tmp_DS));
+    MYCOMPLEX (*tmp_SS)[3] = (MYCOMPLEX(*)[3])calloc(nr, sizeof(*tmp_SS));
+
+    for(MYINT ir=0; ir<nr; ++ir){
+        for(MYINT ii=0; ii<3; ++ii){
+            if(ii<2){
+                tmp_EXP[ir][ii] = RZERO;
+                tmp_VF[ir][ii] = RZERO; 
+                tmp_DD[ir][ii] = RZERO;
+            }
+            tmp_HF[ir][ii] = RZERO;
+            tmp_DS[ir][ii] = RZERO;
+            tmp_SS[ir][ii] = RZERO;
+        }
+        merge_Pk(
+            (sum_EXP_J)? sum_EXP_J[ir] : NULL, 
+            (sum_VF_J)?  sum_VF_J[ir]  : NULL, 
+            (sum_HF_J)?  sum_HF_J[ir]  : NULL, 
+            (sum_DC_J)?  sum_DC_J[ir]  : NULL, 
+            tmp_EXP[ir], tmp_VF[ir],  tmp_HF[ir], 
+            tmp_DD[ir], tmp_DS[ir], tmp_SS[ir]);
+
+        MYCOMPLEX mtmp;
+        for(MYINT ii=0; ii<3; ++ii) {
+            if(ii<2){
+                if(EXPgrn!=NULL){
+                    mtmp = coef*tmp_EXP[ir][ii]; // m=0 爆炸源
+                    EXPgrn[ir][ii] = CREAL(mtmp);
+                }
+                if(VFgrn!=NULL){
+                    mtmp = coef*tmp_VF[ir][ii]; // m=0 垂直力源
+                    VFgrn[ir][ii] = CREAL(mtmp);
+                }
+                if(DDgrn!=NULL){
+                    mtmp = coef*tmp_DD[ir][ii]; // m=0 45-倾滑
+                    DDgrn[ir][ii] = CREAL(mtmp);
+                }
+            }
+            if(HFgrn!=NULL){
+                mtmp = coef*tmp_HF[ir][ii]; // m=1 水平力源
+                HFgrn[ir][ii] = CREAL(mtmp);
+            }
+            if(DSgrn!=NULL){
+                mtmp = coef*tmp_DS[ir][ii]; // m=1 90-倾滑
+                DSgrn[ir][ii] = CREAL(mtmp);
+            }
+            if(SSgrn!=NULL){
+                mtmp = coef*tmp_SS[ir][ii]; // m=2 走滑 
+                SSgrn[ir][ii] = CREAL(mtmp);
+            }
+
+        }
+    }
+
+    free(tmp_EXP);
+    free(tmp_VF);
+    free(tmp_HF);
+    free(tmp_DD);
+    free(tmp_DS);
+    free(tmp_SS);
+}
+
+
+
+void integ_static_grn(
+    PYMODEL1D *pymod1d, MYINT nr, MYREAL *rs, MYREAL vmin_ref, MYREAL keps, MYREAL k0, MYREAL Length,
+
+    // 返回值，维度2代表Z、R分量，维度3代表Z、R、T分量
+    MYREAL EXPgrn[nr][2], // EXZ, EXR 的实部和虚部
+    MYREAL VFgrn[nr][2],  // VFZ, VFR 的实部和虚部
+    MYREAL HFgrn[nr][3],  // HFZ, HFR, HFT 的实部和虚部
+    MYREAL DDgrn[nr][2],  // DDZ, DDR 的实部和虚部      [DD: 45-dip slip]
+    MYREAL DSgrn[nr][3],  // DSZ, DSR, DST 的实部和虚部 [DS: 90-dip slip]
+    MYREAL SSgrn[nr][3],  // SSZ, SSR, SST 的实部和虚部 [SS: strike slip]
+
+    bool calc_upar,
+    MYREAL EXPgrn_uiz[nr][2], // EXZ, EXR 的实部和虚部
+    MYREAL VFgrn_uiz[nr][2],  // VFZ, VFR 的实部和虚部
+    MYREAL HFgrn_uiz[nr][3],  // HFZ, HFR, HFT 的实部和虚部
+    MYREAL DDgrn_uiz[nr][2],  // DDZ, DDR 的实部和虚部      [DD: 45-dip slip]
+    MYREAL DSgrn_uiz[nr][3],  // DSZ, DSR, DST 的实部和虚部 [DS: 90-dip slip]
+    MYREAL SSgrn_uiz[nr][3],  // SSZ, SSR, SST 的实部和虚部 [SS: strike slip]
+    MYREAL EXPgrn_uir[nr][2], // EXZ, EXR 的实部和虚部
+    MYREAL VFgrn_uir[nr][2],  // VFZ, VFR 的实部和虚部
+    MYREAL HFgrn_uir[nr][3],  // HFZ, HFR, HFT 的实部和虚部
+    MYREAL DDgrn_uir[nr][2],  // DDZ, DDR 的实部和虚部      [DD: 45-dip slip]
+    MYREAL DSgrn_uir[nr][3],  // DSZ, DSR, DST 的实部和虚部 [DS: 90-dip slip]
+    MYREAL SSgrn_uir[nr][3],  // SSZ, SSR, SST 的实部和虚部 [SS: strike slip]
+
+    const char *statsstr // 积分结果输出
+){
+    MYREAL rmin=rs[findMinMax_MYREAL(rs, nr, false)];  // 最小震中距
+    MYREAL rmax=rs[findMinMax_MYREAL(rs, nr, true)];   // 最大震中距
+
+    // pymod1d -> mod1d
+    MODEL1D *mod1d = init_mod1d(pymod1d->n);
+    get_mod1d(pymod1d, mod1d);
+
+    const MYREAL hs = (FABS(pymod1d->depsrc - pymod1d->deprcv) < MIN_DEPTH_GAP_SRC_RCV)? 
+                      MIN_DEPTH_GAP_SRC_RCV : FABS(pymod1d->depsrc - pymod1d->deprcv); // hs=max(震源和台站深度差,1.0)
+    // 乘相应系数
+    k0 *= PI/hs;
+
+    if(vmin_ref < RZERO)  keps = -RONE;  // 若使用峰谷平均法，则不使用keps进行收敛判断
+
+    MYREAL k=0.0;
+    const MYREAL dk=FABS(PI2/(Length*rmax));     // 波数积分间隔
+    const MYREAL kmax = k0;
+    // 局部变量，用于求和 sum F(ki,w)Jm(ki*r)ki 
+    // 维度3代表阶数m=0,1,2，维度4代表4种类型的F(k,w)Jm(kr)k的类型，详见int_Pk()函数内的注释
+    MYCOMPLEX (*sum_EXP_J)[3][4] = (EXPgrn != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_EXP_J)) : NULL;
+    MYCOMPLEX (*sum_VF_J)[3][4] = (VFgrn != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_VF_J)) : NULL;
+    MYCOMPLEX (*sum_HF_J)[3][4] = (HFgrn != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_HF_J)) : NULL;
+    MYCOMPLEX (*sum_DC_J)[3][4] = (DDgrn != NULL || DSgrn != NULL || SSgrn != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_DC_J)) : NULL;
+    
+    MYCOMPLEX (*sum_EXP_uiz_J)[3][4] = (EXPgrn_uiz != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_EXP_uiz_J)) : NULL;
+    MYCOMPLEX (*sum_VF_uiz_J)[3][4] = (VFgrn_uiz != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_VF_uiz_J)) : NULL;
+    MYCOMPLEX (*sum_HF_uiz_J)[3][4] = (HFgrn_uiz != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_HF_uiz_J)) : NULL;
+    MYCOMPLEX (*sum_DC_uiz_J)[3][4] = (DDgrn_uiz != NULL || DSgrn_uiz != NULL || SSgrn_uiz != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_DC_uiz_J)) : NULL;
+
+    MYCOMPLEX (*sum_EXP_uir_J)[3][4] = (EXPgrn_uir != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_EXP_uir_J)) : NULL;
+    MYCOMPLEX (*sum_VF_uir_J)[3][4] = (VFgrn_uir != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_VF_uir_J)) : NULL;
+    MYCOMPLEX (*sum_HF_uir_J)[3][4] = (HFgrn_uir != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_HF_uir_J)) : NULL;
+    MYCOMPLEX (*sum_DC_uir_J)[3][4] = (DDgrn_uir != NULL || DSgrn_uir != NULL || SSgrn_uir != NULL) ? (MYCOMPLEX(*)[3][4])calloc(nr, sizeof(*sum_DC_uir_J)) : NULL;
+
+    
+    
+    FILE **fstats = (FILE **)malloc(nr * sizeof(FILE *));
+    FILE **ptam_fstats = (FILE **)malloc(nr * sizeof(FILE *));
+
+    for(int ir=0; ir<nr; ++ir){
+        fstats[ir] = NULL;
+        ptam_fstats[ir] = NULL;
+        if(statsstr!=NULL){
+            char *fname = (char*)malloc((strlen(statsstr)+200)*sizeof(char));
+            if(Length > 0.0){
+                // 常规的波数积分
+                sprintf(fname, "%s/K_%.5f", statsstr, rs[ir]);
+            } else {
+                // Filon积分
+                sprintf(fname, "%s/Filon_%.5f", statsstr, rs[ir]);
+            }
+            
+            fstats[ir] = fopen(fname, "wb");
+
+            if(vmin_ref < 0.0){
+                // 峰谷平均法
+                sprintf(fname, "%s/PTAM_%.5f", statsstr, rs[ir]);
+                ptam_fstats[ir] = fopen(fname, "wb");
+            }
+            free(fname);
+        }
+    }  
+
+    
+    
+
+    // 初始化
+    for(MYINT ir=0; ir<nr; ++ir){
+        for(MYINT m=0; m<3; ++m){
+            for(MYINT v=0; v<4; ++v){
+                if(sum_EXP_J) sum_EXP_J[ir][m][v] = RZERO;
+                if(sum_VF_J) sum_VF_J[ir][m][v] = RZERO;
+                if(sum_HF_J) sum_HF_J[ir][m][v] = RZERO;
+                if(sum_DC_J) sum_DC_J[ir][m][v] = RZERO;
+
+                if(sum_EXP_uiz_J) sum_EXP_uiz_J[ir][m][v] = RZERO;
+                if(sum_VF_uiz_J) sum_VF_uiz_J[ir][m][v] = RZERO;
+                if(sum_HF_uiz_J) sum_HF_uiz_J[ir][m][v] = RZERO;
+                if(sum_DC_uiz_J) sum_DC_uiz_J[ir][m][v] = RZERO;
+
+                if(sum_EXP_uir_J) sum_EXP_uir_J[ir][m][v] = RZERO;
+                if(sum_VF_uir_J) sum_VF_uir_J[ir][m][v] = RZERO;
+                if(sum_HF_uir_J) sum_HF_uir_J[ir][m][v] = RZERO;
+                if(sum_DC_uir_J) sum_DC_uir_J[ir][m][v] = RZERO;
+            }
+        }
+    }
+
+
+
+    if(Length > RZERO){
+        // 常规的波数积分
+        k = discrete_integ(
+            mod1d, dk, kmax, keps, 0.0, nr, rs, 
+            sum_EXP_J, sum_VF_J, sum_HF_J, sum_DC_J, 
+            calc_upar,
+            sum_EXP_uiz_J, sum_VF_uiz_J, sum_HF_uiz_J, sum_DC_uiz_J,
+            sum_EXP_uir_J, sum_VF_uir_J, sum_HF_uir_J, sum_DC_uir_J,
+            fstats, static_kernel);
+    } 
+    else {
+        // 基于线性插值的Filon积分
+        k = linear_filon_integ(
+            mod1d, dk, kmax, keps, 0.0, nr, rs, 
+            sum_EXP_J, sum_VF_J, sum_HF_J, sum_DC_J, 
+            calc_upar,
+            sum_EXP_uiz_J, sum_VF_uiz_J, sum_HF_uiz_J, sum_DC_uiz_J,
+            sum_EXP_uir_J, sum_VF_uir_J, sum_HF_uir_J, sum_DC_uir_J,
+            fstats, static_kernel);
+    }
+
+    // k之后的部分使用峰谷平均法进行显式收敛，建议在浅源地震的时候使用   
+    if(vmin_ref < RZERO){
+        PTA_method(
+            mod1d, k, dk, rmin, rmax, 0.0, nr, rs, 
+            sum_EXP_J, sum_VF_J, sum_HF_J, sum_DC_J, 
+            calc_upar,
+            sum_EXP_uiz_J, sum_VF_uiz_J, sum_HF_uiz_J, sum_DC_uiz_J,
+            sum_EXP_uir_J, sum_VF_uir_J, sum_HF_uir_J, sum_DC_uir_J,
+            fstats, ptam_fstats, static_kernel);
+    }
+
+
+    
+    MYCOMPLEX src_mu = (mod1d->lays + mod1d->isrc)->mu;
+    MYCOMPLEX fac = dk * RONE/(RFOUR*PI * src_mu);
+    
+    // 将积分结果记录到浮点数数组中
+    recordin_GRN(
+        nr, fac, 
+        sum_EXP_J, sum_VF_J, sum_HF_J, sum_DC_J,
+        EXPgrn, VFgrn, HFgrn, DDgrn, DSgrn, SSgrn);
+    if(calc_upar){
+        recordin_GRN(
+            nr, fac, 
+            sum_EXP_uiz_J, sum_VF_uiz_J, sum_HF_uiz_J, sum_DC_uiz_J,
+            EXPgrn_uiz, VFgrn_uiz, HFgrn_uiz, DDgrn_uiz, DSgrn_uiz, SSgrn_uiz);
+        recordin_GRN(
+            nr, fac, 
+            sum_EXP_uir_J, sum_VF_uir_J, sum_HF_uir_J, sum_DC_uir_J,
+            EXPgrn_uir, VFgrn_uir, HFgrn_uir, DDgrn_uir, DSgrn_uir, SSgrn_uir);
+    }
+
+
+    // Free allocated memory for temporary variables
+    if (sum_EXP_J) free(sum_EXP_J);
+    if (sum_VF_J) free(sum_VF_J);
+    if (sum_HF_J) free(sum_HF_J);
+    if (sum_DC_J) free(sum_DC_J);
+
+    if (sum_EXP_uiz_J) free(sum_EXP_uiz_J);
+    if (sum_VF_uiz_J) free(sum_VF_uiz_J);
+    if (sum_HF_uiz_J) free(sum_HF_uiz_J);
+    if (sum_DC_uiz_J) free(sum_DC_uiz_J);
+
+    if (sum_EXP_uir_J) free(sum_EXP_uir_J);
+    if (sum_VF_uir_J) free(sum_VF_uir_J);
+    if (sum_HF_uir_J) free(sum_HF_uir_J);
+    if (sum_DC_uir_J) free(sum_DC_uir_J);
+
+    free_mod1d(mod1d);
+
+    for(MYINT ir=0; ir<nr; ++ir){
+        if(fstats[ir]!=NULL){
+            fclose(fstats[ir]);
+        }
+        if(ptam_fstats[ir]!=NULL){
+            fclose(ptam_fstats[ir]);
+        }
+    }
+
+    free(fstats);
+    free(ptam_fstats);
+}
\ No newline at end of file
diff --git a/pygrt/C_extension/src/static/stgrt_main.c b/pygrt/C_extension/src/static/stgrt_main.c
new file mode 100644
index 00000000..b57f829c
--- /dev/null
+++ b/pygrt/C_extension/src/static/stgrt_main.c
@@ -0,0 +1,547 @@
+/**
+ * @file   stgrt_main.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ *    计算静态位移
+ * 
+ */
+
+#include <stdio.h>
+#include <string.h>
+#include <unistd.h>
+#include <stdlib.h>
+#include <sys/stat.h>
+#include <errno.h>
+
+#include "static/stgrt.h"
+#include "common/const.h"
+#include "common/model.h"
+#include "common/colorstr.h"
+#include "common/logo.h"
+#include "common/integral.h"
+#include "common/iostats.h"
+#include "common/search.h"
+
+static char *command;
+static PYMODEL1D *pymod;
+static double depsrc, deprcv;
+
+//****************** 在该文件以内的全局变量 ***********************//
+// 命令名称
+static char *command = NULL;
+// 模型路径，模型PYMODEL1D指针，全局最大最小速度
+static char *s_modelpath = NULL;
+static char *s_modelname = NULL;
+static PYMODEL1D *pymod;
+static double vmax, vmin;
+// 震源和场点深度
+static double depsrc, deprcv;
+static char *s_depsrc = NULL, *s_deprcv = NULL;
+// 波数积分间隔
+static double Length=0.0, Length0=15.0; // 默认Length
+// 波数积分相关变量
+static double keps=-1.0, k0=5.0;
+// 参考最小速度，小于0表示使用峰谷平均法;
+static double vmin_ref=0.0;
+static const double min_vmin_ref=0.1;
+// 自动使用峰谷平均法的最小厚度差
+static const double hs_ptam = MIN_DEPTH_GAP_SRC_RCV;
+// 接收点位置数组
+static MYREAL *rs = NULL;
+static MYREAL *xs = NULL;
+static MYREAL *ys = NULL;
+static int nr=0, nx=0, ny=0;
+
+// 输出波数积分过程文件
+static char *s_statsdir = NULL;
+
+// 是否计算位移空间导数
+static bool calc_upar=false;
+
+// 各选项的标志变量，初始化为0，定义了则为1
+static int M_flag=0, D_flag=0, 
+            L_flag=0, V_flag=0,
+            K_flag=0, S_flag=0,
+            X_flag=0, Y_flag=0, 
+            e_flag=0;
+
+// 三分量代号
+const char chs[3] = {'Z', 'R', 'T'};
+
+/**
+ * 打印使用说明
+ */
+static void print_help(){
+print_logo();
+printf("\n"
+"[stgrt]\n\n"
+"    Compute static Green's Functions, output to stdout. \n"
+"    The units and components are consistent with the dynamics, \n"
+"    check \"grt -h\" for details.\n"
+"\n"
+"\n\n"
+"Usage:\n"
+"----------------------------------------------------------------\n"
+"    stgrt -M<model> -D<depsrc>/<deprcv> -X<x1>/<x2>/<nx> \n"
+"          -Y<y1>/<y2>/<ny>  [-L<length>] [-V<vmin_ref>] \n" 
+"          [-K<k0>[/<keps>]] [-S]  [-e]\n"
+"\n\n"
+"Options:\n"
+"----------------------------------------------------------------\n"
+"    -M<model>    Filepath to 1D horizontally layered halfspace \n"
+"                 model. The model file has 6 columns: \n"
+"\n"
+"         +-------+----------+----------+-------------+----+----+\n"
+"         | H(km) | Vp(km/s) | Vs(km/s) | Rho(g/cm^3) | Qp | Qa |\n"
+"         +-------+----------+----------+-------------+----+----+\n"
+"\n"
+"                 and the number of layers are unlimited.\n"
+"\n"
+"    -D<depsrc>/<deprcv>\n"
+"                 <depsrc>: source depth (km).\n"
+"                 <deprcv>: receiver depth (km).\n"
+"\n"
+"    -X<x1>/<x2>/<nx>\n"
+"                 Set the equidistant points in the north direction.\n"
+"                 <x1>: start coordinate (km).\n"
+"                 <x2>: end coordinate (km).\n"
+"                 <nx>: number of points.\n"
+"\n"
+"    -Y<y1>/<y2>/<ny>\n"
+"                 Set the equidistant points in the east direction.\n"
+"                 <y1>: start coordinate (km).\n"
+"                 <y2>: end coordinate (km).\n"
+"                 <ny>: number of points.\n"
+"\n"
+"    -L<length>   Define the wavenumber integration interval\n"
+"                 dk=(2*PI)/(<length>*rmax). rmax is the maximum \n"
+"                 epicentral distance. \n"
+"                 There are 3 cases:\n"
+"                 + (default) not set or set %.1f.\n", Length); printf(
+"                   <length> will be %.1f.\n", Length0); printf(
+"                 + manually set POSITIVE value.\n"
+"                 + manually set NEGATIVE value, \n"
+"                   and FIM will be used.\n"
+"\n"
+"    -V<vmin_ref> \n"
+"                 (Inherited from the dynamic case, and the numerical\n"
+"                 value will not be used in here, except its sign.)\n"
+"                 + (default) not set or set %.1f.\n", vmin_ref); printf(
+"                   <vmin_ref> will be the minimum velocity\n"
+"                   of model, but limited to %.1f. and if the \n", min_vmin_ref); printf(
+"                   depth gap between source and receiver is \n"
+"                   thinner than %.1f km, PTAM will be appled\n", hs_ptam); printf(
+"                   automatically.\n"
+"                 + manually set POSITIVE value. \n"
+"                 + manually set NEGATIVE value, \n"
+"                   and PTAM will be appled.\n"
+"\n"
+"    -K<k0>[/<keps>]\n"
+"                 Several parameters designed to define the\n"
+"                 behavior in wavenumber integration. The upper\n"
+"                 bound is k0,\n"
+"                 <k0>:   default is %.1f, and \n", k0); printf(
+"                         multiply PI/hs in program, \n"
+"                         where hs = max(fabs(depsrc-deprcv), %.1f).\n", MIN_DEPTH_GAP_SRC_RCV); printf(
+"                 <keps>: a threshold for break wavenumber \n"
+"                         integration in advance. See \n"
+"                         (Yao and Harkrider, 1983) for details.\n"
+"                         Default %.1f not use.\n", keps); printf(
+"\n"
+"    -S           Output statsfile in wavenumber integration.\n"
+"\n"
+"    -e           Compute the spatial derivatives, ui_z and ui_r,\n"
+"                 of displacement u. In columns, prefix \"r\" means \n"
+"                 ui_r and \"z\" means ui_z. The units of derivatives\n"
+"                 for different sources are: \n"
+"                 + Explosion:     1e-25 /(dyne-cm)\n"
+"                 + Single Force:  1e-20 /(dyne)\n"
+"                 + Double Couple: 1e-25 /(dyne-cm)\n"
+"                 + Moment Tensor: 1e-25 /(dyne-cm)\n"
+"\n"
+"    -h           Display this help message.\n"
+"\n\n"
+"Examples:\n"
+"----------------------------------------------------------------\n"
+"    stgrt -Mmilrow -D2/0 -X-10/10/20 -Y-10/10/20 > grn\n"
+"\n\n\n"
+);
+}
+
+
+/**
+ * 从路径字符串中找到用/或\\分隔的最后一项
+ * 
+ * @param    path     路径字符串指针
+ * 
+ * @return   指向最后一项字符串的指针
+ */
+static char* get_basename(char* path) {
+    // 找到最后一个 '/'
+    char* last_slash = strrchr(path, '/'); 
+    
+#ifdef _WIN32
+    char* last_backslash = strrchr(path, '\\');
+    if (last_backslash && (!last_slash || last_backslash > last_slash)) {
+        last_slash = last_backslash;
+    }
+#endif
+    if (last_slash) {
+        // 返回最后一个 '/' 之后的部分
+        return last_slash + 1; 
+    }
+    // 如果没有 '/'，整个路径就是最后一项
+    return path; 
+}
+
+
+/**
+ * 从命令行中读取选项，处理后记录到全局变量中
+ * 
+ * @param     argc      命令行的参数个数
+ * @param     argv      多个参数字符串指针
+ */
+static void getopt_from_command(int argc, char **argv){
+    int opt;
+    while ((opt = getopt(argc, argv, ":M:D:L:K:X:Y:V:Seh")) != -1) {
+        switch (opt) {
+            // 模型路径，其中每行分别为 
+            //      厚度(km)  Vp(km/s)  Vs(km/s)  Rho(g/cm^3)  Qp   Qs
+            // 互相用空格隔开即可
+            case 'M':
+                M_flag = 1;
+                s_modelpath = (char*)malloc(sizeof(char)*(strlen(optarg)+1));
+                strcpy(s_modelpath, optarg);
+                if(access(s_modelpath, F_OK) == -1){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error! File \"%s\" set by -M not exists.\n" DEFAULT_RESTORE, command, s_modelpath);
+                    exit(EXIT_FAILURE);
+                }
+            
+                s_modelname = get_basename(s_modelpath);
+                break;
+
+            // 震源和场点深度， -Ddepsrc/deprcv
+            case 'D':
+                D_flag = 1;
+                s_depsrc = (char*)malloc(sizeof(char)*(strlen(optarg)+1));
+                s_deprcv = (char*)malloc(sizeof(char)*(strlen(optarg)+1));
+                if(2 != sscanf(optarg, "%[^/]/%s", s_depsrc, s_deprcv)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -D.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                if(1 != sscanf(s_depsrc, "%lf", &depsrc)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -D.\n"  DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                }
+                if(1 != sscanf(s_deprcv, "%lf", &deprcv)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -D.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                }
+                if(depsrc < 0.0 || deprcv < 0.0){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error! Negative value in -D is not supported.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                }
+                break;
+
+            // 波数积分间隔 -LLength
+            case 'L':
+                L_flag = 1;
+                if(0 == sscanf(optarg, "%lf", &Length)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -L.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                break;
+
+            // 参考最小速度 -Vvmin_ref
+            case 'V':
+                V_flag = 1;
+                if(0 == sscanf(optarg, "%lf", &vmin_ref)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -V.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                break;
+
+            // 波数积分相关变量 -Kk0/keps
+            case 'K':
+                K_flag = 1;
+                if(0 == sscanf(optarg, "%lf/%lf", &k0, &keps)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -K.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                
+                if(k0 < 0.0){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error! Can't set negative k0(%f) in -K.\n" DEFAULT_RESTORE, command, k0);
+                    exit(EXIT_FAILURE);
+                }
+                break;
+
+            // X坐标数组，-Xx1/x2/nx
+            case 'X':
+                X_flag = 1;
+                {
+                    MYREAL a1, a2;
+                    if(3 != sscanf(optarg, "%lf/%lf/%d", &a1, &a2, &nx)){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error in -X.\n" DEFAULT_RESTORE, command);
+                        exit(EXIT_FAILURE);
+                    };
+                    if(nx <= 0){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error! Can't set nonpositive nx(%d) in -X.\n" DEFAULT_RESTORE, command, nx);
+                        exit(EXIT_FAILURE);
+                    }
+                    if(a1 > a2){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error! x1(%f) > x2(%f) in -X.\n" DEFAULT_RESTORE, command, a1, a2);
+                        exit(EXIT_FAILURE);
+                    }
+
+                    xs = (MYREAL*)calloc(nx, sizeof(MYREAL));
+                    MYREAL delta = (a2 - a1)/((nx>1)? nx-1 : 1);
+                    for(int i=0; i<nx; ++i){
+                        xs[i] = a1 + delta*i;
+                    }
+                }
+                break;
+
+            // Y坐标数组，-Yy1/y2/ny
+            case 'Y':
+                Y_flag = 1;
+                {
+                    MYREAL a1, a2;
+                    if(3 != sscanf(optarg, "%lf/%lf/%d", &a1, &a2, &ny)){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error in -Y.\n" DEFAULT_RESTORE, command);
+                        exit(EXIT_FAILURE);
+                    };
+                    if(ny <= 0){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error! Can't set nonpositive ny(%d) in -Y.\n" DEFAULT_RESTORE, command, ny);
+                        exit(EXIT_FAILURE);
+                    }
+                    if(a1 > a2){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error! y1(%f) > y2(%f) in -Y.\n" DEFAULT_RESTORE, command, a1, a2);
+                        exit(EXIT_FAILURE);
+                    }
+
+                    ys = (MYREAL*)calloc(ny, sizeof(MYREAL));
+                    MYREAL delta = (a2 - a1)/((ny>1)? ny-1 : 1);
+                    for(int i=0; i<ny; ++i){
+                        ys[i] = a1 + delta*i;
+                    }
+                }
+                break;
+
+            // 输出波数积分中间文件
+            case 'S':
+                S_flag = 1;
+                s_statsdir = (char*)malloc(sizeof(char)*100);
+                sprintf(s_statsdir, "stgrtstats");
+                // 建立保存目录
+                if(mkdir(s_statsdir, 0777) != 0){
+                    if(errno != EEXIST){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to create folder %s. Error code: %d\n" DEFAULT_RESTORE, command, s_statsdir, errno);
+                        exit(EXIT_FAILURE);
+                    }
+                }
+                sprintf(s_statsdir, "%s/%s_stats_%.5f_%.5f", s_statsdir, s_modelname, depsrc, deprcv);
+                if(mkdir(s_statsdir, 0777) != 0){
+                    if(errno != EEXIST){
+                        fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to create folder %s. Error code: %d\n" DEFAULT_RESTORE, command, s_statsdir, errno);
+                        exit(EXIT_FAILURE);
+                    }
+                }
+                break;
+
+            // 是否计算位移空间导数
+            case 'e':
+                e_flag = 1;
+                calc_upar = true;
+                break;
+            
+            // 帮助
+            case 'h':
+                print_help();
+                exit(EXIT_SUCCESS);
+                break;
+
+            // 参数缺失
+            case ':':
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' requires an argument. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+
+            // 非法选项
+            case '?':
+            default:
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' is invalid. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+        }
+    }
+
+    // 检查必须设置的参数是否有设置
+    if(argc == 1){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set options. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+    if(M_flag == 0){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set -M. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+    if(D_flag == 0){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set -D. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+    if(X_flag == 0){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set -X. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+    if(Y_flag == 0){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set -Y. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+
+    // 设置震中距数组
+    nr = nx*ny;
+    rs = (MYREAL*)calloc(nr, sizeof(MYREAL));
+    for(int iy=0; iy<ny; ++iy){
+        for(int ix=0; ix<nx; ++ix){
+            rs[ix + iy*nx] = SQRT(xs[ix]*xs[ix] + ys[iy]*ys[iy]);
+            if(rs[ix + iy*nx] < 1e-5)  rs[ix + iy*nx] = 1e-5;  // 避免0震中距
+        }
+    }
+
+
+}
+
+
+
+
+
+int main(int argc, char **argv){
+    command = argv[0];
+
+    // 传入参数 
+    getopt_from_command(argc, argv);
+
+    // 读入模型文件
+    if((pymod = read_pymod_from_file(command, s_modelpath, depsrc, deprcv)) ==NULL){
+        exit(EXIT_FAILURE);
+    }
+
+    // 最大最小速度
+    vmax = pymod->Va[findMinMax_MYREAL(pymod->Va, pymod->n, true)];
+    vmin = pymod->Vb[findMinMax_MYREAL(pymod->Vb, pymod->n, false)];
+    if(vmin > vmax) {
+        double tmp;
+        tmp = vmin; vmin = vmax; vmax = tmp;
+    }
+
+    // 参考最小速度
+    if(vmin_ref == 0.0){
+        vmin_ref = vmin;
+        if(vmin_ref < min_vmin_ref) vmin_ref = min_vmin_ref;
+    } 
+
+    // 如果没有主动设置vmin_ref，则判断是否要自动使用PTAM
+    if(V_flag == 0 && fabs(deprcv - depsrc) <= hs_ptam) {
+        vmin_ref = - fabs(vmin_ref);
+    }
+    
+    // 设置积分间隔默认值
+    if(Length == 0.0)  Length = Length0;
+
+    // 建立格林函数的complex数组
+    MYREAL (*EXPgrn)[2] = (MYREAL(*)[2])calloc(nr, sizeof(*EXPgrn));
+    MYREAL (*VFgrn)[2]  = (MYREAL(*)[2])calloc(nr, sizeof(*VFgrn));
+    MYREAL (*HFgrn)[3]  = (MYREAL(*)[3])calloc(nr, sizeof(*HFgrn));
+    MYREAL (*DDgrn)[2]  = (MYREAL(*)[2])calloc(nr, sizeof(*DDgrn));
+    MYREAL (*DSgrn)[3]  = (MYREAL(*)[3])calloc(nr, sizeof(*DSgrn));
+    MYREAL (*SSgrn)[3]  = (MYREAL(*)[3])calloc(nr, sizeof(*SSgrn));
+
+    MYREAL (*EXPgrn_uiz)[2] = (calc_upar)? (MYREAL(*)[2])calloc(nr, sizeof(*EXPgrn)) : NULL;
+    MYREAL (*VFgrn_uiz)[2]  = (calc_upar)? (MYREAL(*)[2])calloc(nr, sizeof(*VFgrn)) : NULL;
+    MYREAL (*HFgrn_uiz)[3]  = (calc_upar)? (MYREAL(*)[3])calloc(nr, sizeof(*HFgrn)) : NULL;
+    MYREAL (*DDgrn_uiz)[2]  = (calc_upar)? (MYREAL(*)[2])calloc(nr, sizeof(*DDgrn)) : NULL;
+    MYREAL (*DSgrn_uiz)[3]  = (calc_upar)? (MYREAL(*)[3])calloc(nr, sizeof(*DSgrn)) : NULL;
+    MYREAL (*SSgrn_uiz)[3]  = (calc_upar)? (MYREAL(*)[3])calloc(nr, sizeof(*SSgrn)) : NULL;
+
+    MYREAL (*EXPgrn_uir)[2] = (calc_upar)? (MYREAL(*)[2])calloc(nr, sizeof(*EXPgrn)) : NULL;
+    MYREAL (*VFgrn_uir)[2]  = (calc_upar)? (MYREAL(*)[2])calloc(nr, sizeof(*VFgrn)) : NULL;
+    MYREAL (*HFgrn_uir)[3]  = (calc_upar)? (MYREAL(*)[3])calloc(nr, sizeof(*HFgrn)) : NULL;
+    MYREAL (*DDgrn_uir)[2]  = (calc_upar)? (MYREAL(*)[2])calloc(nr, sizeof(*DDgrn)) : NULL;
+    MYREAL (*DSgrn_uir)[3]  = (calc_upar)? (MYREAL(*)[3])calloc(nr, sizeof(*DSgrn)) : NULL;
+    MYREAL (*SSgrn_uir)[3]  = (calc_upar)? (MYREAL(*)[3])calloc(nr, sizeof(*SSgrn)) : NULL;
+
+    //==============================================================================
+    // 计算静态格林函数
+    integ_static_grn(
+        pymod, nr, rs, vmin_ref, keps, k0, Length, 
+        EXPgrn, VFgrn, HFgrn, DDgrn, DSgrn, SSgrn,
+        calc_upar, 
+        EXPgrn_uiz, VFgrn_uiz, HFgrn_uiz, DDgrn_uiz, DSgrn_uiz, SSgrn_uiz, 
+        EXPgrn_uir, VFgrn_uir, HFgrn_uir, DDgrn_uir, DSgrn_uir, SSgrn_uir, 
+        s_statsdir
+    );
+    //==============================================================================
+
+    MYREAL src_va = pymod->Va[pymod->isrc];
+    MYREAL src_vb = pymod->Vb[pymod->isrc];
+    MYREAL src_rho = pymod->Rho[pymod->isrc];
+    MYREAL rcv_va = pymod->Va[pymod->ircv];
+    MYREAL rcv_vb = pymod->Vb[pymod->ircv];
+    MYREAL rcv_rho = pymod->Rho[pymod->ircv];
+
+    // 输出物性参数
+    fprintf(stdout, "# %15.5e %15.5e %15.5e\n", src_va, src_vb, src_rho);
+    fprintf(stdout, "# %15.5e %15.5e %15.5e\n", rcv_va, rcv_vb, rcv_rho);
+
+    // 定义标题数组
+    const char *titles[15] = {
+        "EXZ", "EXR", "VFZ", "VFR", "HFZ", "HFR", "HFT",
+        "DDZ", "DDR", "DSZ", "DSR", "DST", "SSZ", "SSR", "SST"
+    };
+    const char *upar_titles[30] = {
+        "zEXZ", "zEXR", "zVFZ", "zVFR", "zHFZ", "zHFR", "zHFT",
+        "zDDZ", "zDDR", "zDSZ", "zDSR", "zDST", "zSSZ", "zSSR", "zSST",
+        "rEXZ", "rEXR", "rVFZ", "rVFR", "rHFZ", "rHFR", "rHFT",
+        "rDDZ", "rDDR", "rDSZ", "rDSR", "rDST", "rSSZ", "rSSR", "rSST"
+    };
+
+    // 输出标题
+    fprintf(stdout, "#%14s", "X(km)");
+    fprintf(stdout, "%15s", "Y(km)");
+    for(int i=0; i<15; ++i) fprintf(stdout, "%15s", titles[i]);
+    if(calc_upar) {
+        for(int i=0; i<30; ++i)  fprintf(stdout, "%15s", upar_titles[i]);
+    }
+    fprintf(stdout, "\n");
+
+    // 写结果
+    for(int iy=0; iy<ny; ++iy) {
+        for(int ix=0; ix<nx; ++ix) {
+            int ir = ix + iy * nx;
+            fprintf(stdout, "%15.5e%15.5e", xs[ix], ys[iy]);
+            MYREAL *grns[] = {
+                EXPgrn[ir], VFgrn[ir], HFgrn[ir], DDgrn[ir], DSgrn[ir], SSgrn[ir]
+            };
+            int grn_sizes[] = {2, 2, 3, 2, 3, 3};
+            // 对Z分量反向
+            for(int i=0; i<6; ++i) {
+                for (int j=0; j<grn_sizes[i]; ++j)
+                    fprintf(stdout, "%15.5e", (j == 0 ? -1.0 : 1.0) * grns[i][j]);
+            }
+            if(calc_upar) {
+                // 前6个是对z的偏导，注意后续对z符号的判断
+                MYREAL *upar_grns[] = {
+                    EXPgrn_uiz[ir], VFgrn_uiz[ir], HFgrn_uiz[ir], DDgrn_uiz[ir], DSgrn_uiz[ir], SSgrn_uiz[ir],
+                    EXPgrn_uir[ir], VFgrn_uir[ir], HFgrn_uir[ir], DDgrn_uir[ir], DSgrn_uir[ir], SSgrn_uir[ir]
+                };
+                for(int i=0; i<12; ++i) {
+                    for(int j=0; j<grn_sizes[i % 6]; ++j)
+                        fprintf(stdout, "%15.5e", (j == 0 ? -1.0 : 1.0) * (i < 6 ? -1.0 : 1.0) * upar_grns[i][j]);
+                }
+            }
+            fprintf(stdout, "\n");
+        }
+    }
+
+}
+
diff --git a/pygrt/C_extension/src/static/stgrt_strain.c b/pygrt/C_extension/src/static/stgrt_strain.c
new file mode 100644
index 00000000..0ca1b8e8
--- /dev/null
+++ b/pygrt/C_extension/src/static/stgrt_strain.c
@@ -0,0 +1,227 @@
+/**
+ * @file   stgrt_strain.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-08
+ * 
+ *    根据预先合成的静态位移空间导数，组合成静态应变
+ * 
+ */
+
+
+#include <stdio.h>
+#include <unistd.h>
+#include <stdlib.h>
+#include <ctype.h>
+#include <string.h>
+#include <stdbool.h>
+
+#include "common/const.h"
+#include "common/logo.h"
+#include "common/colorstr.h"
+
+extern char *optarg;
+extern int optind;
+extern int optopt;
+
+//****************** 在该文件以内的全局变量 ***********************//
+// 命令名称
+static char *command = NULL;
+
+// 输出分量格式，即是否需要旋转到ZNE
+static bool rot2ZNE = false;
+
+/**
+ * 打印使用说明
+ */
+static void print_help(){
+print_logo();
+printf("\n"
+"[stgrt.strain]\n\n"
+"    Conbine spatial derivatives of static displacements (read from stdin)\n"
+"    into strain.\n"
+"\n\n"
+"Usage:\n"
+"----------------------------------------------------------------\n"
+"    stgrt.strain < <file>\n"
+"\n\n\n"
+);
+}
+
+
+/**
+ * 从命令行中读取选项，处理后记录到全局变量中
+ * 
+ * @param     argc      命令行的参数个数
+ * @param     argv      多个参数字符串指针
+ */
+static void getopt_from_command(int argc, char **argv){
+    int opt;
+    while ((opt = getopt(argc, argv, ":h")) != -1) {
+        switch (opt) {
+
+            // 帮助
+            case 'h':
+                print_help();
+                exit(EXIT_SUCCESS);
+                break;
+
+            // 参数缺失
+            case ':':
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' requires an argument. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+
+            // 非法选项
+            case '?':
+            default:
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' is invalid. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+        }
+    }
+}
+
+
+int main(int argc, char **argv){
+    command = argv[0]; 
+
+    getopt_from_command(argc, argv);
+
+    // 从标准输入中读取合成的静态位移及其空间导数
+    double x0, y0, syn[3], syn_upar[3][3];  // [3][3]表示u_{i,j}
+
+    // 建立一个指针数组，方便读取多列数据
+    double *pt_grn[14];
+    // 按照特定顺序
+    {
+        double **pt = &pt_grn[0];
+        *(pt++) = &x0;
+        *(pt++) = &y0;
+        for(int k=0; k<3; ++k)  *(pt++) = &syn[k];
+        for(int k=0; k<3; ++k){
+            for(int i=0; i<3; ++i){
+                *(pt++) = &syn_upar[i][k]; //  u_i / x_k
+            }
+        }
+    }
+
+    // 是否已打印输出的列名
+    bool printHead = false;
+
+    // 输入列数
+    int ncols = 0;
+
+    // 物性参数
+    double src_va=0.0, src_vb=0.0, src_rho=0.0;
+    double rcv_va=0.0, rcv_vb=0.0, rcv_rho=0.0;
+
+    // 震中距
+    double dist = 0.0;
+
+    // 三分量
+    const char zrtchs[3] = {'Z', 'R', 'T'};
+    const char znechs[3] = {'Z', 'N', 'E'};
+    const char *chs = NULL;
+
+    // 逐行读入
+    char line[1024];
+    int iline = 0;
+    while(fgets(line, sizeof(line), stdin)){
+        iline++;
+        if(iline == 1){
+            // 读取震源物性参数
+            if(3 != sscanf(line, "# %lf %lf %lf", &src_va, &src_vb, &src_rho)){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to read src property from \"%s\". \n" DEFAULT_RESTORE, command, line);
+                exit(EXIT_FAILURE);
+            }
+        }
+        else if(iline == 2){
+            // 读取场点物性参数
+            if(3 != sscanf(line, "# %lf %lf %lf", &rcv_va, &rcv_vb, &rcv_rho)){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to read rcv property from \"%s\". \n" DEFAULT_RESTORE, command, line);
+                exit(EXIT_FAILURE);
+            }
+        }
+        else if(iline == 3){
+            // 根据列长度判断是否有位移空间导数
+            char *copyline = strdup(line+1);  // +1去除首个#字符
+            char *token = strtok(copyline, " ");
+            while (token != NULL) {
+                // 根据列名尾字符判断是否需要旋转到ZNE，出现一次即可
+                if(!rot2ZNE && strlen(token) > 0 && token[strlen(token)-1]=='N')   rot2ZNE = true;
+                ncols++;
+                token = strtok(NULL, " ");
+            }
+            free(copyline);
+
+            // 指示特定的通道名
+            chs = (rot2ZNE)? znechs : zrtchs;
+
+            // 想合成位移空间导数但输入的格林函数没有
+            if(ncols < 14){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! The input has no spatial derivatives. \n" DEFAULT_RESTORE, command);
+                exit(EXIT_FAILURE);
+            }
+        }
+        if(line[0] == '#')  continue;
+
+        // 读取该行数据
+        char *copyline = strdup(line);
+        char *token = strtok(copyline, " ");
+        for(int i=0; i<ncols; ++i){
+            sscanf(token, "%lf", pt_grn[i]);  token = strtok(NULL, " ");
+        }
+        free(copyline);
+
+        // 计算震中距
+        dist = sqrt(x0*x0 + y0*y0);
+        if(dist < 1e-5)  dist=1e-5;
+
+        // 先输出列名
+        if(!printHead){
+            // 打印物性参数
+            fprintf(stdout, "# %15.5e %15.5e %15.5e\n", src_va, src_vb, src_rho);
+            fprintf(stdout, "# %15.5e %15.5e %15.5e\n", rcv_va, rcv_vb, rcv_rho);
+            
+            fprintf(stdout, "#%14s%15s", "X(km)", "Y(km)");
+            char s_channel[15];
+            for(int k=0; k<3; ++k){
+                for(int i=k; i<3; ++i){
+                    sprintf(s_channel, "%c%c", toupper(chs[k]), toupper(chs[i])); 
+                    fprintf(stdout, "%15s", s_channel);
+                }
+            }
+            fprintf(stdout, "\n");
+            printHead = true;
+        }
+
+        // 打印xy位置
+        fprintf(stdout, "%15.5e%15.5e", x0, y0);
+
+        // 循环6个分量
+        char c1, c2;
+        for(int i1=0; i1<3; ++i1){
+            c1 = chs[i1];
+            for(int i2=i1; i2<3; ++i2){
+                c2 = chs[i2];
+
+                double val = 0.0;
+
+                val = 0.5 * (syn_upar[i1][i2] + syn_upar[i2][i1]);
+
+                // 特殊情况需加上协变导数，1e-5是因为km->cm
+                if(c1=='R' && c2=='T'){
+                    val -= 0.5 * syn[2] / dist * 1e-5;
+                } else if(c1=='T' && c2=='T'){
+                    val += syn[1] / dist * 1e-5;
+                }
+
+                // 打印结果
+                fprintf(stdout, "%15.5e", val);
+            }
+        }
+
+        fprintf(stdout, "\n");
+    }
+
+}
\ No newline at end of file
diff --git a/pygrt/C_extension/src/static/stgrt_stress.c b/pygrt/C_extension/src/static/stgrt_stress.c
new file mode 100644
index 00000000..1d7426dd
--- /dev/null
+++ b/pygrt/C_extension/src/static/stgrt_stress.c
@@ -0,0 +1,242 @@
+/**
+ * @file   stgrt_stress.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-04-08
+ * 
+ *    根据预先合成的静态位移空间导数，组合成静态应力
+ * 
+ */
+
+
+
+#include <stdio.h>
+#include <unistd.h>
+#include <stdlib.h>
+#include <ctype.h>
+#include <string.h>
+#include <stdbool.h>
+
+#include "common/const.h"
+#include "common/logo.h"
+#include "common/colorstr.h"
+
+extern char *optarg;
+extern int optind;
+extern int optopt;
+
+//****************** 在该文件以内的全局变量 ***********************//
+// 命令名称
+static char *command = NULL;
+
+// 输出分量格式，即是否需要旋转到ZNE
+static bool rot2ZNE = false;
+
+/**
+ * 打印使用说明
+ */
+static void print_help(){
+print_logo();
+printf("\n"
+"[stgrt.stress]\n\n"
+"    Conbine spatial derivatives of static displacements (read from stdin)\n"
+"    into stress (unit: dyne/cm^2 = 0.1 Pa).\n"
+"\n\n"
+"Usage:\n"
+"----------------------------------------------------------------\n"
+"    stgrt.stress < <file>\n"
+"\n\n\n"
+);
+}
+
+
+/**
+ * 从命令行中读取选项，处理后记录到全局变量中
+ * 
+ * @param     argc      命令行的参数个数
+ * @param     argv      多个参数字符串指针
+ */
+static void getopt_from_command(int argc, char **argv){
+    int opt;
+    while ((opt = getopt(argc, argv, ":h")) != -1) {
+        switch (opt) {
+
+            // 帮助
+            case 'h':
+                print_help();
+                exit(EXIT_SUCCESS);
+                break;
+
+            // 参数缺失
+            case ':':
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' requires an argument. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+
+            // 非法选项
+            case '?':
+            default:
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' is invalid. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+        }
+    }
+}
+
+
+int main(int argc, char **argv){
+    command = argv[0]; 
+
+    getopt_from_command(argc, argv);
+
+    // 从标准输入中读取合成的静态位移及其空间导数
+    double x0, y0, syn[3], syn_upar[3][3];  // [3][3]表示u_{i,j}
+
+    // 建立一个指针数组，方便读取多列数据
+    double *pt_grn[14];
+    // 按照特定顺序
+    {
+        double **pt = &pt_grn[0];
+        *(pt++) = &x0;
+        *(pt++) = &y0;
+        for(int k=0; k<3; ++k)  *(pt++) = &syn[k];
+        for(int k=0; k<3; ++k){
+            for(int i=0; i<3; ++i){
+                *(pt++) = &syn_upar[i][k]; //  u_i / x_k
+            }
+        }
+    }
+
+    // 是否已打印输出的列名
+    bool printHead = false;
+
+    // 输入列数
+    int ncols = 0;
+
+    // 物性参数
+    double src_va=0.0, src_vb=0.0, src_rho=0.0;
+    double rcv_va=0.0, rcv_vb=0.0, rcv_rho=0.0, rcv_mu=0.0, rcv_lam=0.0;
+
+    // 震中距
+    double dist = 0.0;
+
+    // 三分量
+    const char zrtchs[3] = {'Z', 'R', 'T'};
+    const char znechs[3] = {'Z', 'N', 'E'};
+    const char *chs = NULL;
+
+    // 体积应变和lambda的乘积
+    double lam_ukk=0.0;
+
+    // 逐行读入
+    char line[1024];
+    int iline = 0;
+    while(fgets(line, sizeof(line), stdin)){
+        iline++;
+        if(iline == 1){
+            // 读取震源物性参数
+            if(3 != sscanf(line, "# %lf %lf %lf", &src_va, &src_vb, &src_rho)){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to read src property from \"%s\". \n" DEFAULT_RESTORE, command, line);
+                exit(EXIT_FAILURE);
+            }
+        }
+        else if(iline == 2){
+            // 读取场点物性参数
+            if(3 != sscanf(line, "# %lf %lf %lf", &rcv_va, &rcv_vb, &rcv_rho)){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to read rcv property from \"%s\". \n" DEFAULT_RESTORE, command, line);
+                exit(EXIT_FAILURE);
+            }
+
+            rcv_mu = rcv_vb*rcv_vb*rcv_rho*1e10;
+            rcv_lam = rcv_va*rcv_va*rcv_rho*1e10 - 2.0*rcv_mu;
+        }
+        else if(iline == 3){
+            // 根据列长度判断是否有位移空间导数
+            char *copyline = strdup(line+1);  // +1去除首个#字符
+            char *token = strtok(copyline, " ");
+            while (token != NULL) {
+                // 根据列名尾字符判断是否需要旋转到ZNE，出现一次即可
+                if(!rot2ZNE && strlen(token) > 0 && token[strlen(token)-1]=='N')   rot2ZNE = true;
+                ncols++;
+                token = strtok(NULL, " ");
+            }
+            free(copyline);
+
+            // 指示特定的通道名
+            chs = (rot2ZNE)? znechs : zrtchs;
+
+            // 想合成位移空间导数但输入的格林函数没有
+            if(ncols < 14){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! The input has no spatial derivatives. \n" DEFAULT_RESTORE, command);
+                exit(EXIT_FAILURE);
+            }
+        }
+        if(line[0] == '#')  continue;
+
+        // 读取该行数据
+        char *copyline = strdup(line);
+        char *token = strtok(copyline, " ");
+        for(int i=0; i<ncols; ++i){
+            sscanf(token, "%lf", pt_grn[i]);  token = strtok(NULL, " ");
+        }
+        free(copyline);
+
+        // 计算震中距
+        dist = sqrt(x0*x0 + y0*y0);
+        if(dist < 1e-5)  dist=1e-5;
+
+        // 先计算体积应变u_kk = u_11 + u22 + u33 和 lamda的乘积，ZRT分量需包括协变导数
+        lam_ukk = syn_upar[0][0] + syn_upar[1][1] + syn_upar[2][2];
+        if(!rot2ZNE)  lam_ukk += syn[1]/dist*1e-5;
+        lam_ukk *= rcv_lam;
+
+        // 先输出列名
+        if(!printHead){
+            // 打印物性参数
+            fprintf(stdout, "# %15.5e %15.5e %15.5e\n", src_va, src_vb, src_rho);
+            fprintf(stdout, "# %15.5e %15.5e %15.5e\n", rcv_va, rcv_vb, rcv_rho);
+            
+            fprintf(stdout, "#%14s%15s", "X(km)", "Y(km)");
+            char s_channel[15];
+            for(int k=0; k<3; ++k){
+                for(int i=k; i<3; ++i){
+                    sprintf(s_channel, "%c%c", toupper(chs[k]), toupper(chs[i])); 
+                    fprintf(stdout, "%15s", s_channel);
+                }
+            }
+            fprintf(stdout, "\n");
+            printHead = true;
+        }
+
+        // 打印xy位置
+        fprintf(stdout, "%15.5e%15.5e", x0, y0);
+
+        // 循环6个分量
+        char c1, c2;
+        for(int i1=0; i1<3; ++i1){
+            c1 = chs[i1];
+            for(int i2=i1; i2<3; ++i2){
+                c2 = chs[i2];
+
+                double val = 0.0;
+
+                val = rcv_mu * (syn_upar[i1][i2] + syn_upar[i2][i1]);
+
+                // 对角线分量
+                if(c1 == c2)  val += lam_ukk;
+
+                // 特殊情况需加上协变导数，1e-5是因为km->cm
+                if(c1=='R' && c2=='T'){
+                    val -= rcv_mu * syn[2] / dist * 1e-5;
+                } else if(c1=='T' && c2=='T'){
+                    val += 2.0 * rcv_mu * syn[1] / dist * 1e-5;
+                }
+
+                // 打印结果
+                fprintf(stdout, "%15.5e", val);
+            }
+        }
+
+        fprintf(stdout, "\n");
+    }
+
+}
\ No newline at end of file
diff --git a/pygrt/C_extension/src/static/stgrt_syn.c b/pygrt/C_extension/src/static/stgrt_syn.c
new file mode 100644
index 00000000..2de3ea52
--- /dev/null
+++ b/pygrt/C_extension/src/static/stgrt_syn.c
@@ -0,0 +1,517 @@
+/**
+ * @file   stgrt_syn.c
+ * @author Zhu Dengda (zhudengda@mail.iggcas.ac.cn)
+ * @date   2025-02-18
+ * 
+ *    根据计算好的静态格林函数，定义震源机制以及方位角等，生成合成的静态三分量位移场
+ * 
+ */
+
+
+
+#include <stdio.h>
+#include <unistd.h>
+#include <string.h>
+#include <stdlib.h>
+#include <stdbool.h>
+#include <ctype.h>
+
+#include "common/const.h"
+#include "common/logo.h"
+#include "common/colorstr.h"
+#include "common/radiation.h"
+#include "common/coord.h"
+
+extern char *optarg;
+extern int optind;
+extern int optopt;
+
+//****************** 在该文件以内的全局变量 ***********************//
+// 命令名称
+static char *command = NULL;
+// 放大系数，对于位错源、爆炸源、张量震源，M0是标量地震矩；对于单力源，M0是放大系数
+static double M0 = 0.0;
+// 在放大系数上是否需要乘上震源处的剪切模量
+static bool mult_src_mu = false;
+// 存储不同震源的震源机制相关参数的数组
+static double mchn[6] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
+// 最终要计算的震源类型
+static int computeType=GRT_SYN_COMPUTE_EX;
+static char s_computeType[3] = "EX";
+
+// 是否计算位移空间导数
+static bool calc_upar=false;
+
+// 输出分量格式，即是否需要旋转到ZNE
+static bool rot2ZNE = false;
+
+// 各选项的标志变量，初始化为0，定义了则为1
+static int S_flag=0, M_flag=0, F_flag=0,
+           T_flag=0, N_flag=0,
+           e_flag=0;
+
+
+// 三分量代号
+static const char zrtchs[3] = {'Z', 'R', 'T'};
+static const char znechs[3] = {'Z', 'N', 'E'};
+
+// 计算和位移相关量的种类（1-位移，2-ui_z，3-ui_r，4-ui_t）
+static int calcUTypes=1;
+
+/**
+ * 打印使用说明
+ */
+static void print_help(){
+print_logo();
+printf("\n"
+"[stgrt.syn]\n\n"
+"    Compute static displacement with the outputs of \n"
+"    command `stgrt` (reading from stdin).\n"
+"    Three components are:\n"
+"       + Up (Z),\n"
+"       + Radial Outward (R),\n"
+"       + Transverse Clockwise (T),\n"
+"    and the units are cm. You can add -N to rotate ZRT to ZNE.\n"
+"\n\n"
+"Usage:\n"
+"----------------------------------------------------------------\n"
+"    stgrt.syn -S<scale> \n"
+"              [-M<strike>/<dip>/<rake>]\n"
+"              [-T<Mxx>/<Mxy>/<Mxz>/<Myy>/<Myz>/<Mzz>]\n"
+"              [-F<fn>/<fe>/<fz>] \n"
+"              [-N] [-e]\n"
+"              < <grn>\n"
+"\n"
+"\n\n"
+"Options:\n"
+"----------------------------------------------------------------\n"
+"    -S[u]<scale>  Scale factor to all kinds of source. \n"
+"                  + For Explosion, Double Couple and Moment Tensor,\n"
+"                    unit of <scale> is dyne-cm. \n"
+"                  + For Single Force, unit of <scale> is dyne.\n"
+"                  + Since \"\\mu\" exists in scalar seismic moment\n"
+"                    (\\mu*A*D), you can simply set -Su<scale>, <scale>\n"
+"                    equals A*D (Area*Slip, [cm^3]), and <scale> will \n"
+"                    multiply \\mu automatically in program.\n"
+"\n"
+"    For source type, you can only set at most one of\n"
+"    '-M', '-T' and '-F'. If none, an Explosion is used.\n"
+"\n"
+"    -M<strike>/<dip>/<rake>\n"
+"                  Three angles to define a fault. \n"
+"                  The angles are in degree.\n"
+"\n"
+"    -T<Mxx>/<Mxy>/<Mxz>/<Myy>/<Myz>/<Mzz>\n"
+"                  Six elements of Moment Tensor. \n"
+"                  Notice they will be scaled by <scale>.\n"
+"\n"
+"    -F<fn>/<fe>/<fz>\n"
+"                  North, East and Vertical(Downward) Forces.\n"
+"                  Notice they will be scaled by <scale>.\n"
+"\n"
+"    -N            Components of results will be Z, N, E.\n"
+"\n"
+"    -e            Compute the spatial derivatives, ui_z and ui_r,\n"
+"                  of displacement u. In filenames, prefix \"r\" means \n"
+"                  ui_r and \"z\" means ui_z. \n"
+"\n"
+"    -h            Display this help message.\n"
+"\n\n"
+"Examples:\n"
+"----------------------------------------------------------------\n"
+"    Say you have computed Static Green's functions with following command:\n"
+"        stgrt -Mmilrow -D2/0 -X-5/5/10 -Y-5/5/10 > grn\n"
+"\n"
+"    Then you can get static displacement of Explosion\n"
+"        stgrt.syn -Su1e16 < grn > syn_exp\n"
+"\n"
+"    or Double Couple\n"
+"        stgrt.syn -Su1e16 -M100/20/80 < grn > syn_dc\n"
+"\n"
+"    or Single Force\n"
+"        stgrt.syn -S1e20 -F0.5/-1.2/3.3 < grn > syn_sf\n"
+"\n"
+"    or Moment Tensor\n"
+"        stgrt.syn -Su1e16 -T2.3/0.2/-4.0/0.3/0.5/1.2 < grn > syn_mt\n"
+"\n\n\n"
+"\n"
+);
+}
+
+
+/**
+ * 从命令行中读取选项，处理后记录到全局变量中
+ * 
+ * @param     argc      命令行的参数个数
+ * @param     argv      多个参数字符串指针
+ */
+static void getopt_from_command(int argc, char **argv){
+    int opt;
+    while ((opt = getopt(argc, argv, ":S:M:F:T:Neh")) != -1) {
+        switch (opt) {
+            // 放大系数
+            case 'S':
+                S_flag = 1;
+                {   
+                    // 检查是否存在字符u，若存在表明需要乘上震源处的剪切模量
+                    char *upos=NULL;
+                    if((upos=strchr(optarg, 'u')) != NULL){
+                        mult_src_mu = true;
+                        *upos = ' ';
+                    }
+                }
+                
+                if(0 == sscanf(optarg, "%lf", &M0)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -S.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                break;
+
+            // 位错震源
+            case 'M':
+                M_flag = 1; 
+                computeType = GRT_SYN_COMPUTE_DC;
+                double strike, dip, rake;
+                sprintf(s_computeType, "%s", "DC");
+                if(3 != sscanf(optarg, "%lf/%lf/%lf", &strike, &dip, &rake)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -M.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                if(strike < 0.0 || strike > 360.0){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error! Strike in -M must be in [0, 360].\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                }
+                if(dip < 0.0 || dip > 90.0){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error! Dip in -M must be in [0, 90].\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                }
+                if(rake < -180.0 || rake > 180.0){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error! Rake in -M must be in [-180, 180].\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                }
+                mchn[0] = strike;
+                mchn[1] = dip;
+                mchn[2] = rake;
+                break;
+
+            // 单力源
+            case 'F':
+                F_flag = 1;
+                computeType = GRT_SYN_COMPUTE_SF;
+                double fn, fe, fz;
+                sprintf(s_computeType, "%s", "SF");
+                if(3 != sscanf(optarg, "%lf/%lf/%lf", &fn, &fe, &fz)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -F.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                mchn[0] = fn;
+                mchn[1] = fe;
+                mchn[2] = fz;
+                break;
+
+            // 张量震源
+            case 'T':
+                T_flag = 1;
+                computeType = GRT_SYN_COMPUTE_MT;
+                double Mxx, Mxy, Mxz, Myy, Myz, Mzz;
+                sprintf(s_computeType, "%s", "MT");
+                if(6 != sscanf(optarg, "%lf/%lf/%lf/%lf/%lf/%lf", &Mxx, &Mxy, &Mxz, &Myy, &Myz, &Mzz)){
+                    fprintf(stderr, "[%s] " BOLD_RED "Error in -T.\n" DEFAULT_RESTORE, command);
+                    exit(EXIT_FAILURE);
+                };
+                mchn[0] = Mxx;
+                mchn[1] = Mxy;
+                mchn[2] = Mxz;
+                mchn[3] = Myy;
+                mchn[4] = Myz;
+                mchn[5] = Mzz;
+                break;
+
+            // 是否计算位移空间导数
+            case 'e':
+                e_flag = 1;
+                calc_upar = true;
+                calcUTypes = 4;
+                break;
+
+            // 是否旋转到ZNE
+            case 'N':
+                N_flag = 1;
+                rot2ZNE = true;
+                break;
+
+            // 帮助
+            case 'h':
+                print_help();
+                exit(EXIT_SUCCESS);
+                break;
+
+            // 参数缺失
+            case ':':
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' requires an argument. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+
+            // 非法选项
+            case '?':
+            default:
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Option '-%c' is invalid. Use '-h' for help.\n" DEFAULT_RESTORE, command, optopt);
+                exit(EXIT_FAILURE);
+                break;
+        }
+    }
+
+    // 检查必选项有没有设置
+    if(argc == 1){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set options. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+    if(S_flag == 0){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Need set -S. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+
+    // 只能使用一种震源
+    if(M_flag + F_flag + T_flag > 1){
+        fprintf(stderr, "[%s] " BOLD_RED "Error! Only support at most one of '-M', '-F' and '-T'. Use '-h' for help.\n" DEFAULT_RESTORE, command);
+        exit(EXIT_FAILURE);
+    }
+}
+
+
+
+
+//====================================================================================
+//====================================================================================
+//====================================================================================
+int main(int argc, char **argv){
+    command = argv[0];
+    getopt_from_command(argc, argv);
+
+    // 辐射因子
+    double srcCoef[3][6];
+
+    // 从标准输入中读取静态格林函数表
+    double x0, y0, grn[3][6], syn[3], syn_upar[3][3];
+    double grn_uiz[3][6], grn_uir[3][6];
+
+    // 根据参数设置，选择分量名
+    const char *chs = (rot2ZNE)? znechs : zrtchs;
+
+    for(int i=0; i<3; ++i){
+        for(int k=0; k<6; ++k){
+            srcCoef[i][k] = 0.0;
+            grn[i][k] = 0.0;
+            grn_uiz[i][k] = 0.0;
+            grn_uir[i][k] = 0.0;
+        }
+    }
+
+    // 建立一个指针数组，方便读取多列数据
+    double *pt_grn[47];
+    int grn_sizes[6] = {2, 2, 3, 2, 3, 3};
+    // 按照特定顺序
+    {
+        double **pt = &pt_grn[0];
+        *(pt++) = &x0;
+        *(pt++) = &y0;
+        for(int m=0; m<3; ++m){
+            for(int k=0; k<6; ++k){
+                for(int i=0; i<grn_sizes[k]; ++i){
+                    if(m==0){
+                        *pt = &grn[i][k];
+                    } else if(m==1){
+                        *pt = &grn_uiz[i][k];
+                    } else if(m==2){
+                        *pt = &grn_uir[i][k];
+                    }
+                    pt++;
+                }
+            }
+        }
+    }
+
+    // 是否已打印输出的列名
+    bool printHead = false;
+
+    // 输入列数
+    int ncols = 0;
+
+    // 方位角
+    double azrad = 0.0;
+
+    // 物性参数
+    double src_va=0.0, src_vb=0.0, src_rho=0.0, src_mu=0.0;
+    double rcv_va=0.0, rcv_vb=0.0, rcv_rho=0.0;
+
+    // 用于计算位移空间导数的比例系数
+    double upar_scale=1.0; 
+
+    // 震中距
+    double dist = 0.0;
+
+    char line[1024];
+    int iline = 0;
+    while(fgets(line, sizeof(line), stdin)){
+        iline++;
+        if(iline == 1){
+            // 读取震源物性参数
+            if(3 != sscanf(line, "# %lf %lf %lf", &src_va, &src_vb, &src_rho)){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to read src property from \"%s\". \n" DEFAULT_RESTORE, command, line);
+                exit(EXIT_FAILURE);
+            }
+            src_mu = src_vb*src_vb*src_rho*1e10;
+
+            if(mult_src_mu)  M0 *= src_mu;
+        }
+        else if(iline == 2){
+            // 读取场点物性参数
+            if(3 != sscanf(line, "# %lf %lf %lf", &rcv_va, &rcv_vb, &rcv_rho)){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! Unable to read rcv property from \"%s\". \n" DEFAULT_RESTORE, command, line);
+                exit(EXIT_FAILURE);
+            }
+        }
+        else if(iline == 3){
+            // 根据列长度判断是否有位移空间导数
+            char *copyline = strdup(line+1);  // +1去除首个#字符
+            char *token = strtok(copyline, " ");
+            while (token != NULL) {
+                ncols++;
+                token = strtok(NULL, " ");
+            }
+            free(copyline);
+
+            // 想合成位移空间导数但输入的格林函数没有
+            if(calc_upar && ncols < 47){
+                fprintf(stderr, "[%s] " BOLD_RED "Error! The input has no spatial derivatives. \n" DEFAULT_RESTORE, command);
+                exit(EXIT_FAILURE);
+            }
+        }
+        if(line[0] == '#')  continue;
+
+        // 读取该行数据
+        char *copyline = strdup(line);
+        char *token = strtok(copyline, " ");
+        for(int i=0; i<ncols; ++i){
+            sscanf(token, "%lf", pt_grn[i]);  token = strtok(NULL, " ");
+        }
+        free(copyline);
+
+        // 计算方位角
+        azrad = atan2(y0, x0);
+
+        // 计算震中距
+        dist = sqrt(x0*x0 + y0*y0);
+        if(dist < 1e-5)  dist=1e-5;
+
+        // 先输出列名
+        if(!printHead){
+            // 打印物性参数
+            fprintf(stdout, "# %15.5e %15.5e %15.5e\n", src_va, src_vb, src_rho);
+            fprintf(stdout, "# %15.5e %15.5e %15.5e\n", rcv_va, rcv_vb, rcv_rho);
+
+            fprintf(stdout, "#%14s%15s", "X(km)", "Y(km)");
+            char s_channel[5];
+            for(int i=0; i<3; ++i){
+                sprintf(s_channel, "%s%c", s_computeType, toupper(chs[i])); 
+                fprintf(stdout, "%15s", s_channel);
+            }
+
+            if(calc_upar){
+                for(int k=0; k<3; ++k){
+                    for(int i=0; i<3; ++i){
+                        sprintf(s_channel, "%c%s%c", tolower(chs[k]), s_computeType, toupper(chs[i])); 
+                        fprintf(stdout, "%15s", s_channel);
+                    }
+                }
+            }
+
+            fprintf(stdout, "\n");
+            printHead = true;
+        }
+
+        double (*grn3)[6];  // 使用对应类型的格林函数
+        double tmpsyn[3];
+        for(int ityp=0; ityp<calcUTypes; ++ityp){
+            // 求位移空间导数时，需调整比例系数
+            switch (ityp){
+                // 合成位移
+                case 0:
+                    upar_scale=1.0;
+                    break;
+
+                // 合成ui_z
+                case 1:
+                // 合成ui_r
+                case 2:
+                    upar_scale=1e-5;
+                    break;
+
+                // 合成ui_t
+                case 3:
+                    upar_scale=1e-5 / dist;
+                    break;
+                    
+                default:
+                    break;
+            }
+
+            if(ityp==1){
+                grn3 = grn_uiz;
+            } else if(ityp==2){
+                grn3 = grn_uir;
+            } else {
+                grn3 = grn;
+            }
+
+            tmpsyn[0] = tmpsyn[1] = tmpsyn[2] = 0.0;
+            // 计算震源辐射因子
+            set_source_radiation(srcCoef, computeType, ityp==3, M0, upar_scale, azrad, mchn);
+
+            for(int i=0; i<3; ++i){
+                for(int k=0; k<6; ++k){
+                    tmpsyn[i] += grn3[i][k] * srcCoef[i][k];
+                }
+            }
+
+            // if(ityp == 0)  fprintf(stdout, "%15.5e%15.5e", x0, y0);
+            // fprintf(stdout, "%15.5e%15.5e%15.5e", syn[0], syn[1], syn[2]);
+
+            // 保存数据
+            for(int i=0; i<3; ++i){
+                if(ityp == 0){
+                    syn[i] = tmpsyn[i];
+                } else {
+                    syn_upar[ityp-1][i] = tmpsyn[i];
+                }
+            }
+        }
+
+        // 是否要转到ZNE
+        if(rot2ZNE){
+            if(calc_upar){
+                rot_zrt2zxy_upar(azrad, syn, syn_upar, dist*1e5);
+            } else {
+                rot_zxy2zrt_vec(-azrad, syn);
+            }
+        }
+
+        // 输出数据
+        fprintf(stdout, "%15.5e%15.5e", x0, y0);
+        for(int i=0; i<3; ++i){
+            fprintf(stdout, "%15.5e", syn[i]);
+        }
+        if(calc_upar){
+            for(int i=0; i<3; ++i){
+                for(int k=0; k<3; ++k){
+                    fprintf(stdout, "%15.5e", syn_upar[i][k]);
+                }
+            }
+        }
+        
+
+        fprintf(stdout, "\n");
+        
+    }
+
+}
\ No newline at end of file