Multiple_synthetic_seismic_models_with_fault.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple synthetic seismic models with vertical fault"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I took [Agile Scientific](https://agilescientific.com/)'s [x lines of code, Wedge model](https://github.com/agile-geoscience/xlines/blob/master/notebooks/00_Synthetic_wedge_model.ipynb) and adapt it to make 100 synthetic models: 10 impedance models x 10 wavelets x 10 random noise fields + 1 fault. **N.B. code is optimized for Python 3.7.1**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import scipy.ndimage as snd\n",
    "import copy\n",
    "import bruges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### make an initial layered model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = np.zeros((50,49), dtype=np.int) \n",
    "model[8:16,:]=1\n",
    "model[16:24,:]=2\n",
    "model[24:32,:]=3\n",
    "model[32:40,:]=4\n",
    "model[40:,:]=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 49)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPcAAAD6CAYAAAB9EtGNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAKsElEQVR4nO3dX6jeB33H8ffHJG3SqJi4JoSmLF6EYZG1ZWe1o7sYjYGsE9MbWQuOXBRy46CC4NINBsIueiXe7CbMzoCiFBQSiuBCtIxBaZvaqo2xphubBkOO6xBd67qm+e7i/OoOSU7Pc57nOec858v7BYffv+c5vy8l7/x+z3OenqSqkNTPe9Z7AEmrw7ilpoxbasq4paaMW2rKuKWmJoo7yaEkryR5NcmxaQ0laXIZ9+fcSTYBPwEOAheB54GHq+pHSz3nptxcW9k+1vkkXe9/eJ3/rTdzo2ObJ/i+9wCvVtW/AST5OnAYWDLurWznozkwwSklLfZsnVny2CS35bcBP1u0fXHYJ2kGTHLlvtGtwHX3+EmOAkcBtnLLBKeTtBKTXLkvArcv2t4L/PzaB1XV8aqaq6q5Ldw8wekkrcQkcT8P7E/yoSQ3AQ8Bp6YzlqRJjX1bXlVXkvwl8G1gE/BEVZ2b2mSSJjLJa26q6lvAt6Y0i6Qp8hNqUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTE/1bYStV77uFK/f8wVqeUmqtnntmyWNeuaWmjFtqyrilpoxbasq4paaMW2rKuKWmlo07yRNJ5pO8vGjfziSnk1wYljtWd0xJKzXKlfvLwKFr9h0DzlTVfuDMsC1phiwbd1X9M/Bf1+w+DJwY1k8AD055LkkTGvc19+6qugQwLHct9cAkR5OcTXL2rbdeH/N0klZq1d9Qq6rjVTVXVXNbtmxf7dNJGowb9+UkewCG5fz0RpI0DePGfQo4MqwfAU5OZxxJ0zLKj8K+BjwD/F6Si0keAR4HDia5ABwctiXNkGX/f+6qeniJQwemPIukKfITalJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTW17K9ZmqYr28PlP7x5LU8ptXblXJY85pVbasq4paaMW2rKuKWmjFtqyrilpoxbasq4paaMW2rKuKWmjFtqyrilpoxbasq4paaMW2pq2biT3J7ku0nOJzmX5NFh/84kp5NcGJY7Vn9cSaMa5cp9BfhsVX0YuBf4dJI7gGPAmaraD5wZtiXNiGXjrqpLVfW9Yf3XwHngNuAwcGJ42AngwdUaUtLKreg1d5J9wN3As8DuqroEC38BALumPZyk8Y0cd5L3At8APlNVv1rB844mOZvk7JU3Xh9nRkljGCnuJFtYCPurVfXNYfflJHuG43uA+Rs9t6qOV9VcVc1tvmX7NGaWNIJR3i0P8CXgfFV9YdGhU8CRYf0IcHL640ka1yi/2vg+4C+AHyZ5adj318DjwJNJHgF+CnxydUaUNI5l466qfwGW+uXIB6Y7jqRp8RNqUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01Ncpny6fm6rarvHnnG2t5Sqm1q9uuLnnMK7fUlHFLTRm31JRxS00Zt9SUcUtNGbfUlHFLTRm31JRxS00Zt9SUcUtNGbfUlHFLTRm31JRxS00Zt9SUcUtNGbfUlHFLTRm31JRxS00Zt9SUcUtNLRt3kq1Jnkvy/STnknx+2L8zyekkF4bljtUfV9KoRrlyvwncX1V3AncBh5LcCxwDzlTVfuDMsC1pRiwbdy3472Fzy/BVwGHgxLD/BPDgqkwoaSwjveZOsinJS8A8cLqqngV2V9UlgGG5a4nnHk1yNsnZq79+fVpzS1rGSHFX1dtVdRewF7gnyUdGPUFVHa+quaqae8/7to87p6QVWtG75VX1S+Bp4BBwOckegGE5P/XpJI1tlHfLb03ygWF9G/Ax4MfAKeDI8LAjwMnVGlLSyo3y73PvAU4k2cTCXwZPVtVTSZ4BnkzyCPBT4JOrOKekFVo27qr6AXD3Dfa/BhxYjaEkTc5PqElNjXJbPjU7t73Bn9/xwlqeUmrtH7e9seQxr9xSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNTVy3Ek2JXkxyVPD9s4kp5NcGJY7Vm9MSSu1eQWPfRQ4D7x/2D4GnKmqx5McG7b/6t2+wW2bf8Pf7frhWINKut4/bf7NksdGunIn2Qv8GfAPi3YfBk4M6yeAB8ecT9IqGPW2/IvA54Cri/btrqpLAMNy142emORokrNJzv7itbcnGlbS6JaNO8nHgfmqemGcE1TV8aqaq6q5Wz+4aZxvIWkMo7zmvg/4RJIHgK3A+5N8BbicZE9VXUqyB5hfzUElrcyyV+6qeqyq9lbVPuAh4DtV9SngFHBkeNgR4OSqTSlpxSb5OffjwMEkF4CDw7akGbGSH4VRVU8DTw/rrwEHpj+SpGnwE2pSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01ZdxSU8YtNWXcUlPGLTVl3FJTxi01lapau5MlvwD+A/gd4D/X7MST20jzbqRZYWPNO4uz/m5V3XqjA2sa929Pmpytqrk1P/GYNtK8G2lW2FjzbqRZwdtyqS3jlppar7iPr9N5x7WR5t1Is8LGmncjzbo+r7klrT5vy6Wm1jzuJIeSvJLk1STH1vr87ybJE0nmk7y8aN/OJKeTXBiWO9ZzxnckuT3Jd5OcT3IuyaPD/lmdd2uS55J8f5j388P+mZwXIMmmJC8meWrYntlZb2RN406yCfh74E+BO4CHk9yxljMs48vAoWv2HQPOVNV+4MywPQuuAJ+tqg8D9wKfHv5bzuq8bwL3V9WdwF3AoST3MrvzAjwKnF+0PcuzXq+q1uwL+CPg24u2HwMeW8sZRphxH/Dyou1XgD3D+h7glfWecYm5TwIHN8K8wC3A94CPzuq8wF4WAr4feGoj/Vl452utb8tvA362aPvisG+W7a6qSwDDctc6z3OdJPuAu4FnmeF5h9vcl4B54HRVzfK8XwQ+B1xdtG9WZ72htY47N9jn2/UTSPJe4BvAZ6rqV+s9z7upqrer6i4Wror3JPnIes90I0k+DsxX1QvrPcsk1jrui8Dti7b3Aj9f4xlW6nKSPQDDcn6d5/mtJFtYCPurVfXNYffMzvuOqvol8DQL72/M4rz3AZ9I8u/A14H7k3yF2Zx1SWsd9/PA/iQfSnIT8BBwao1nWKlTwJFh/QgLr23XXZIAXwLOV9UXFh2a1XlvTfKBYX0b8DHgx8zgvFX1WFXtrap9LPwZ/U5VfYoZnPVdrcMbFQ8APwH+Ffib9X7T4ZrZvgZcAt5i4S7jEeCDLLyxcmFY7lzvOYdZ/5iFlzQ/AF4avh6Y4Xl/H3hxmPdl4G+H/TM576K5/4T/f0Ntpme99stPqElN+Qk1qSnjlpoybqkp45aaMm6pKeOWmjJuqSnjlpr6PygqedDGVVEAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(model, cmap='viridis', aspect=1);\n",
    "print (np.shape(model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### make some Vp-rho pairs (rock 0, rock 1, ... , rock5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rocks = np.array([[2700, 2750],  # Vp, rho\n",
    "                  [2400, 2450],\n",
    "                  [2600, 2650], \n",
    "                  [2400, 2450],\n",
    "                  [2800, 3000], \n",
    "                  [3100, 3200],])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### create 10 slightly different variations of the Vp-rho pairs in the next tow cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 6, 2)\n"
     ]
    }
   ],
   "source": [
    "r = np.random.RandomState(1234)\n",
    "rnd = r.rand(10,6,2)*0.2\n",
    "print (np.shape(rnd))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6, 10, 2)\n"
     ]
    }
   ],
   "source": [
    "mrocks = np.array([rocks + rocks*rn for rn in rnd], dtype=np.int)\n",
    "manyrocks = np.rollaxis(mrocks,1,0)\n",
    "print (np.shape(manyrocks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  create 10 earth models by combining Vp-rho pairs with the layered model, dispaly a pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 50, 49, 2)\n"
     ]
    }
   ],
   "source": [
    "earth = manyrocks[model]\n",
    "earth = np.rollaxis(earth,2,0)\n",
    "print (np.shape(earth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot(121)\n",
    "ax2 = plt.subplot(122)\n",
    "\n",
    "ax1.imshow(earth[3,:,:,0],cmap='viridis', aspect=1)\n",
    "ax2.imshow(earth[3,:,:,1],cmap='viridis', aspect=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### create 10 impedance models from the above earht models by multiplying Vp and Rho (last axis), display a few"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 50, 49)\n"
     ]
    }
   ],
   "source": [
    "impedances = [np.apply_along_axis(np.product, -1, e).astype(float) for e in earth]# Python 2\n",
    "print (np.shape(impedances))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot(221)\n",
    "ax2 = plt.subplot(222)\n",
    "ax3 = plt.subplot(223)\n",
    "ax4 = plt.subplot(224)\n",
    "\n",
    "ax1.imshow(impedances[2],cmap='gray', aspect=1)\n",
    "ax2.imshow(impedances[4],cmap='gray', aspect=1)\n",
    "ax3.imshow(impedances[6],cmap='gray', aspect=1)\n",
    "ax4.imshow(impedances[8],cmap='gray', aspect=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insert a vertical fault in the impedance models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 50, 49)\n"
     ]
    }
   ],
   "source": [
    "faulted = copy.deepcopy(impedances)\n",
    "for r, i in zip(faulted, np.arange(len(faulted))):\n",
    "    temp = np.array(r)\n",
    "    rolled = np.roll(np.array(r[:,:24]), 4, axis = 0)\n",
    "    temp[:,:24]=rolled\n",
    "    faulted[i]=temp\n",
    "    \n",
    "print (np.shape(faulted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot(221)\n",
    "ax2 = plt.subplot(222)\n",
    "ax3 = plt.subplot(223)\n",
    "ax4 = plt.subplot(224)\n",
    "\n",
    "ax1.imshow(faulted[2],cmap='gray', aspect=1)\n",
    "ax2.imshow(faulted[4],cmap='gray', aspect=1)\n",
    "ax3.imshow(faulted[6],cmap='gray', aspect=1)\n",
    "ax4.imshow(faulted[8],cmap='gray', aspect=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### generate 10 reflection coefficient models and display a few"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 49, 49)\n"
     ]
    }
   ],
   "source": [
    "rc =  [(flt[1:,:] - flt[:-1,:]) / (flt[1:,:] + flt[:-1,:]) for flt in faulted]\n",
    "print (np.shape(rc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot(221)\n",
    "ax2 = plt.subplot(222)\n",
    "ax3 = plt.subplot(223)\n",
    "ax4 = plt.subplot(224)\n",
    "\n",
    "ax1.imshow(rc[2],cmap='gray', aspect=1)\n",
    "ax2.imshow(rc[4],cmap='gray', aspect=1)\n",
    "ax3.imshow(rc[6],cmap='gray', aspect=1)\n",
    "ax4.imshow(rc[8],cmap='gray', aspect=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### make 10 ricker wavelets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = []\n",
    "ws = [bruges.filters.ricker(duration=0.098, dt=0.002, f=fr) \n",
    "      for fr in [35, 40, 45, 50, 55, 60, 65, 70, 75, 80]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "[plt.plot(w) for w in ws];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### create array with synthetic models (10 faulted rc models x 10 wavelets), display a couple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 49, 49)\n"
     ]
    }
   ],
   "source": [
    "synth = np.array([np.apply_along_axis(lambda t: np.convolve(t, w, mode='same'), axis=0,\n",
    "                             arr=r) for r in rc for w in ws ]) # this way sorted by rc primary\n",
    "                                                                     # e.g.:\n",
    "                                                                     # 1st is:  rc 0, w0,   then \n",
    "                                                                     # 2nd is:  rc 1, w0,   and so on\n",
    "                                                                     # and:\n",
    "                                                                     # 1st is:  rc 0, w0    then \n",
    "                                                                     # 10th is: rc 0, w1,   and so on\n",
    "\n",
    "print (np.shape(synth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot(221)\n",
    "ax2 = plt.subplot(222)\n",
    "ax3 = plt.subplot(223)\n",
    "ax4 = plt.subplot(224)\n",
    "\n",
    "ax1.imshow(synth[0],cmap='gray', aspect=1)\n",
    "ax2.imshow(synth[1],cmap='gray', aspect=1)\n",
    "ax3.imshow(synth[0],cmap='gray', aspect=1)\n",
    "ax4.imshow(synth[10],cmap='gray', aspect=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### adding a bit of blur and some random noise, display again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 49, 49)\n"
     ]
    }
   ],
   "source": [
    "blurred = sp.ndimage.gaussian_filter(synth, sigma=1.1)\n",
    "noisy = blurred + 0.5 * blurred.std() * np.random.random(blurred.shape)\n",
    "print (np.shape(noisy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "ax1 = plt.subplot(221)\n",
    "ax2 = plt.subplot(222)\n",
    "ax3 = plt.subplot(223)\n",
    "ax4 = plt.subplot(224)\n",
    "\n",
    "ax1.imshow(noisy[0],cmap='gray', aspect=1)\n",
    "ax2.imshow(noisy[1],cmap='gray', aspect=1)\n",
    "ax3.imshow(noisy[0],cmap='gray', aspect=1)\n",
    "ax4.imshow(noisy[10],cmap='gray', aspect=1);"
   ]
  }
 ],
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  "anaconda-cloud": {},
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   "display_name": "exp2020",
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}