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3D_RTM_CUDA
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3D_RTM_CUDA
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/*A demo of 3D RTM with GPU accelaration, the input
data file is SU format, so you must set the keyword sx,sy,gx,gy.
This program can automatically malloc the grid size of model based
on the shot and receiver poisitions with a rectangle area.
The output file is also a SU format file with only one line in
the direction of inline, program will output imaging of every shot, so
I suggest you stacking this SU file to get a final imaging lines. */
/* */
/* */
/* Copyright (c), Chinese Academy of Science, Guiting Chen,2018.12.15 */
/* All rights reserved. */
#include "su.h"
#include "segy.h"
#include "header.h"
#include "stdio.h"
#include "stdlib.h"
#include "math.h"
#include <string.h>
#include <math.h>
#include <time.h>
#include <cuda_runtime.h>
/*********************** self documentation **********************/
char *sdoc[] = {
" ",
" 3D reverse time migration with high performance GPU acceleration ",
" Author: Guiting Chen, Chinese Academy of Science,2018.12.09 ",
" ",
NULL };
#define LOOKFAC 2 /* Look ahead factor for npfaro */
#define PFA_MAX 720720 /* Largest allowed nfft */
#ifndef PI
#define PI 3.1415926535
#endif
#ifndef EPS
#define EPS 0.0000000001
#endif
#define BlockSize1 16// tile size in 1st-axis
#define BlockSize2 16// tile size in 2nd-axis
#define BlockSize3 16// tile size in 3nd-axis
#define radius 4// half of the order in space
void sf_check_gpu_error(const char *msg)
/*< check GPU errors >*/
{
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err) {
warn("Cuda error: %s: %s", msg, cudaGetErrorString(err));
exit(0);
}
}
__constant__ float stencil[radius + 1] = { -205.0 / 72.0,8.0 / 5.0,-1.0 / 5.0,8.0 / 315.0,-1.0 / 560.0 };
__global__ void cuda_ricker_wavelet(float *wlt, float fm, float dt, int nt)
/*< generate ricker wavelet with time deley >*/
{
int it = threadIdx.x + blockDim.x*blockIdx.x;
if (it<nt) {
float tmp = PI*fm*fabsf(it*dt - 1.0 / fm);//delay the wavelet to exhibit all waveform
tmp *= tmp;
wlt[it] = (1.0 - 2.0*tmp)*expf(-tmp);// ricker wavelet at time: t=nt*dt
}
}
__global__ void cuda_init_bell3(float *bell)
/*< initialize Gaussian bell function>*/
{
int i1 = threadIdx.x;
int i2 = threadIdx.y;
int i3;
int id = i1 + i2*(2 * nbell + 1);
float s = 0.5*nbell;
bell[id] = expf(-((i1 - nbell)*(i1 - nbell) + (i2 - nbell)*(i2 - nbell)) / s);
}
__global__ void cuda_set_sg(int *szxy, int szbeg, int sxbeg, int sybeg, int jsz, int jsx, int jsy, int ns, int nz, int nx, int nb)
/*< set the positions of sources and geophones in whole domain >*/
{
int id = threadIdx.x + blockDim.x*blockIdx.x;
int nbr = nb + radius;
int nn1 = nz + 2 * nbr;
int nn2 = nx + 2 * nbr;
if (id<ns) szxy[id] = (szbeg + id*jsz + nbr) + nn1*(sxbeg + id*jsx + nbr) + nn1*nn2*(sybeg + id*jsy + nbr);
}
__global__ void cuda_set_ss(int *szxy, int ns_x, int ns_y, int szbeg, int sxbeg, int sybeg, int jsx, int jsy, int ns, int nz, int nx, int nb)
/*< set the positions of sources and geophones in whole domain >*/
{
int i, j, id;
int nbr = nb + radius;
int nn1 = nz + 2 * nbr;
int nn2 = nx + 2 * nbr;
for (i = 0; i<ns_y; i++)
for (j = 0; j < ns_x; j++)
{
id = j + i * 10;
szxy[id] = (szbeg + nbr) + nn1*(sxbeg + j*jsx + nbr) + nn1*nn2*(sybeg + i*jsy + nbr);
}
}
__global__ void cuda_add_source(bool add, float *p, float *source, int *szxy, int ns)
/*< add/subtract sources: length of source[]=ns, index stored in szxy[] >*/
{
int id = threadIdx.x + blockIdx.x*blockDim.x;
if (id<ns) {
if (add) {
p[szxy[id]] += source[id];
}
else {
p[szxy[id]] -= source[id];
}
}
}
//n1=nz+2*nb; n2=nx+2*nb; n3=ny+2*nb;
__global__ void cuda_step_fd3d(float *p0, float *p1, float *vv, float _dz2, float _dx2, float _dy2, int n1, int n2, int n3)
/*< step forward: 3-D FD, order=8 >*/
{
bool validr = true;
bool validw = true;
const int gtid1 = blockIdx.x * blockDim.x + threadIdx.x;
const int gtid2 = blockIdx.y * blockDim.y + threadIdx.y;
const int ltid1 = threadIdx.x;
const int ltid2 = threadIdx.y;
const int work1 = blockDim.x;
const int work2 = blockDim.y;
__shared__ float tile[BlockSize2 + 2 * radius][BlockSize1 + 2 * radius];
const int stride2 = n1 + 2 * radius;
const int stride3 = stride2 * (n2 + 2 * radius);
int inIndex = 0;
int outIndex = 0;
// Advance inputIndex to start of inner volume
inIndex += radius * stride2 + radius;
// Advance inputIndex to target element
inIndex += gtid2 * stride2 + gtid1;
float infront[radius];
float behind[radius];
float current;
const int t1 = ltid1 + radius;
const int t2 = ltid2 + radius;
// Check in bounds
if ((gtid1 >= n1 + radius) || (gtid2 >= n2 + radius)) validr = false;
if ((gtid1 >= n1) || (gtid2 >= n2)) validw = false;
// Preload the "infront" and "behind" data
for (int i = radius - 2; i >= 0; i--)
{
if (validr) behind[i] = p1[inIndex];
inIndex += stride3;
}
if (validr) current = p1[inIndex];
outIndex = inIndex;
inIndex += stride3;
for (int i = 0; i < radius; i++)
{
if (validr) infront[i] = p1[inIndex];
inIndex += stride3;
}
// Step through the zx-planes
#pragma unroll 9
for (int i3 = 0; i3 < n3; i3++)
{
// Advance the slice (move the thread-front)
for (int i = radius - 1; i > 0; i--) behind[i] = behind[i - 1];
behind[0] = current;
current = infront[0];
#pragma unroll 4
for (int i = 0; i < radius - 1; i++) infront[i] = infront[i + 1];
if (validr) infront[radius - 1] = p1[inIndex];
inIndex += stride3;
outIndex += stride3;
__syncthreads();
// Update the data slice in the local tile
// Halo above & below
if (ltid2 < radius)
{
tile[ltid2][t1] = p1[outIndex - radius * stride2];
tile[ltid2 + work2 + radius][t1] = p1[outIndex + work2 * stride2];
}
// Halo left & right
if (ltid1 < radius)
{
tile[t2][ltid1] = p1[outIndex - radius];
tile[t2][ltid1 + work1 + radius] = p1[outIndex + work1];
}
tile[t2][t1] = current;
__syncthreads();
// Compute the output value
float c1, c2, c3;
c1 = c2 = c3 = stencil[0] * current;
#pragma unroll 4
for (int i = 1; i <= radius; i++)
{
c1 += stencil[i] * (tile[t2][t1 - i] + tile[t2][t1 + i]);
c2 += stencil[i] * (tile[t2 - i][t1] + tile[t2 + i][t1]);
c3 += stencil[i] * (infront[i - 1] + behind[i - 1]);
}
c1 *= _dz2;
c2 *= _dx2;
c3 *= _dy2;
if (validw) p0[outIndex] = 2.0*p1[outIndex] - p0[outIndex] + vv[outIndex] * (c1 + c2 + c3);
}
}
void velocity_transform(float*vv, float dt, float dz, float dx, float dy, int nz, int nx, int ny, int nb)
/*< velocity transform: vv<--vv^2 >*/
{
int i1, i2, i3, nbr, nn1, nn2, nn3;
float a;
nbr = radius + nb;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
for (i3 = 0; i3<nn3; i3++)
for (i2 = 0; i2<nn2; i2++)
for (i1 = 0; i1<nn1; i1++)
{
a = vv[i1 + nn1*i2 + nn1*nn2*i3] * dt;
vv[i1 + nn1*i2 + nn1*nn2*i3] = a*a;
}
}
void random_boundary(float *v0, float *vv, int nz, int nx, int ny, int nb)
/*< initialize velocity using random boundary condition >*/
{
int i1, i2, i3, nbr, nn1, nn2, nn3, a;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
/* top and bottom */
for (i3 = 0; i3<nn3; i3++)
for (i2 = 0; i2<nn2; i2++)
for (i1 = 0; i1<nbr; i1++)
{
a = (int)vv[i1 + nn1*i2 + nn1*nn2*i3];
vv[i1 + nn1*i2 + nn1*nn2*i3] -= float(rand() % a) / nbr*(nbr - i1);
a = (int)vv[(nn1 - 1 - i1) + nn1*i2 + nn1*nn2*i3];
vv[(nn1 - 1 - i1) + nn1*i2 + nn1*nn2*i3] -= float(rand() % a) / nbr*(nbr - i1);
}
/* left and right */
for (i3 = 0; i3<nn3; i3++)
for (i2 = 0; i2<nbr; i2++)
for (i1 = 0; i1<nn1; i1++)
{
a = (int)vv[i1 + nn1*i2 + nn1*nn2*i3];
vv[i1 + nn1*i2 + nn1*nn2*i3] -= float(rand() % a) / nbr*(nbr - i2);
a = (int)vv[i1 + nn1*(nn2 - i2 - 1) + nn1*nn2*i3];
vv[i1 + nn1*(nn2 - i2 - 1) + nn1*nn2*i3] -= float(rand() % a) / nbr*(nbr - i2);
}
/* front and rear */
for (i3 = 0; i3<nbr; i3++)
for (i2 = 0; i2<nn2; i2++)
for (i1 = 0; i1<nn1; i1++)
{
a = (int)vv[i1 + nn1*i2 + nn1*nn2*i3];
vv[i1 + nn1*i2 + nn1*nn2*i3] -= float(rand() % a) / nbr*(nbr - i3);
a = (int)vv[i1 + nn1*i2 + nn1*nn2*(nn3 - 1 - i3)];
vv[i1 + nn1*i2 + nn1*nn2*(nn3 - 1 - i3)] -= float(rand() % a) / nbr*(nbr - i3);
}
}
void extend3d(float *v0, float *vv, int nz, int nx, int ny, int nb)
/*< extend 3d velocity model >*/
{
int i1, i2, i3, nbr, nn1, nn2, nn3;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
/* central zone */
for (i3 = 0; i3<ny; i3++)
for (i2 = 0; i2<nx; i2++)
for (i1 = 0; i1<nz; i1++)
{
vv[(i1 + nbr) + nn1*(i2 + nbr) + nn1*nn2*(i3 + nbr)] = v0[i1 + nz*i2 + nz*nx*i3];
}
/* top and bottom */
for (i3 = 0; i3<nn3; i3++)
for (i2 = 0; i2<nn2; i2++)
for (i1 = 0; i1<nbr; i1++)
{
vv[i1 + nn1*i2 + nn1*nn2*i3] = vv[nbr + nn1*i2 + nn1*nn2*i3];
vv[(nn1 - 1 - i1) + nn1*i2 + nn1*nn2*i3] = vv[(nn1 - 1 - nbr) + nn1*i2 + nn1*nn2*i3];
}
/* left and right */
for (i3 = 0; i3<nn3; i3++)
for (i2 = 0; i2<nbr; i2++)
for (i1 = 0; i1<nn1; i1++)
{
vv[i1 + nn1*i2 + nn1*nn2*i3] = vv[i1 + nn1*nbr + nn1*nn2*i3];
vv[i1 + nn1*(nn2 - i2 - 1) + nn1*nn2*i3] = vv[i1 + nn1*(nn2 - nbr - 1) + nn1*nn2*i3];
}
/* front and rear */
for (i3 = 0; i3<nbr; i3++)
for (i2 = 0; i2<nn2; i2++)
for (i1 = 0; i1<nn1; i1++)
{
vv[i1 + nn1*i2 + nn1*nn2*i3] = vv[i1 + nn1*i2 + nn1*nn2*nbr];
vv[i1 + nn1*i2 + nn1*nn2*(nn3 - 1 - i3)] = vv[i1 + nn1*i2 + nn1*nn2*(nn3 - nbr - 1)];
}
}
void window3d(float *a, float *b, int nz, int nx, int ny, int nb)
/*< window a 3d subvolume >*/
{
int i1, i2, i3, nbr, nn1, nn2;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
for (i3 = 0; i3<ny; i3++)
for (i2 = 0; i2<nx; i2++)
for (i1 = 0; i1<nz; i1++)
{
a[i1 + nz*i2 + nz*nx*i3] = b[(i1 + nbr) + nn1*(i2 + nbr) + nn1*nn2*(i3 + nbr)];
}
}
void window2d(float *a, float *b, int nz, int ny, int nb)
/*< window a 2d subvolume >*/
{
int i1, i2, i3, nbr, nn1, nn2;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = ny + 2 * nbr;
for (i2 = 0; i2<ny; i2++)
for (i1 = 0; i1<nz; i1++)
{
a[i1 + nz*i2] = b[(i1 + nbr) + nn1*(i2 + nbr)];
}
}
__global__ void apply_sponge_tb(float *sp0, float *abc, int nz, int nx, int ny, int nb)
/*< extend 3d velocity model >*/
{
int i2 = blockIdx.x*blockDim.x + threadIdx.x;
int i3 = blockIdx.y*blockDim.y + threadIdx.y;
int nbr, nn1, nn2, nn3, i, NN;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
NN = nn1*nn2*nn3;
int id = i2*nn1 + i3*nn1*nn2;
for (i = 0; i < nbr; i++)
{
if (id + i < NN && id + nn1 - 1 - i < NN)
{
sp0[id + i] *= abc[i];
sp0[id + nn1 - 1 - i] *= abc[i];
}
}
}
__global__ void apply_sponge_lr(float *sp0, float *abc, int nz, int nx, int ny, int nb)
/*< extend 3d velocity model >*/
{
int i1 = blockIdx.x*blockDim.x + threadIdx.x;
int i3 = blockIdx.y*blockDim.y + threadIdx.y;
int nbr, nn1, nn2, nn3, i, NN;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
NN = nn1*nn2*nn3;
int id = i1 + i3*nn1*nn2;
for (i = 0; i < nbr; i++)
{
if (id + i*nn1 < NN && id + (nn2 - 1 - i)*nn1 < NN)
{
sp0[id + i*nn1] *= abc[i];
sp0[id + (nn2 - 1 - i)*nn1] *= abc[i];
}
}
}
__global__ void apply_sponge_fr(float *sp0, float *abc, int nz, int nx, int ny, int nb)
/*< extend 3d velocity model >*/
{
int i1 = blockIdx.x*blockDim.x + threadIdx.x;
int i2 = blockIdx.y*blockDim.y + threadIdx.y;
int nbr, nn1, nn2, nn3, i, NN;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
NN = nn1*nn2*nn3;
int id = i1 + i2*nn1;
for (i = 0; i < nbr; i++)
{
if (id + i*nn1*nn2 < NN && id + (nn3 - 1 - i)*nn1*nn2 < NN)
{
sp0[id + i*nn1*nn2] *= abc[i];
sp0[id + (nn3 - 1 - i)*nn1*nn2] *= abc[i];
}
}
}
__global__ void cuda_rw_innertb(float *innertb, float *p, int nz, int nx, int ny, int nb, bool read)
/*< read and write the inner computation zone boundary coefficients from and into RAM along z direction
read==flase, write/save boundary; read==true, read the boundary >*/
{
int i2 = blockIdx.x*blockDim.x + threadIdx.x;
int i3 = blockIdx.y*blockDim.y + threadIdx.y;
int nbr, nn1, nn2, nn3, i, NN;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
NN = nn1*nn2*nn3;
int id = nbr + (i2 + nbr)*nn1 + (i3 + nbr)*nn1*nn2;
int idp = i2 + i3*nx;
if (i2 < nx && i3 < ny)
{
if (read)
{
p[id] = innertb[idp];
p[id + nz - 1] = innertb[idp + (nx*ny)];
}
else
{
innertb[idp] = p[id];
innertb[idp + (nx*ny)] = p[id + nz - 1];
}
}
}
__global__ void cuda_rw_innerlr(float *innerlr, float *p, int nz, int nx, int ny, int nb, bool read)
/*< read and write the inner computation zone boundary coefficients from and into RAM along z direction
read==flase, write/save boundary; read==true, read the boundary >*/
{
int i1 = blockIdx.x*blockDim.x + threadIdx.x;
int i3 = blockIdx.y*blockDim.y + threadIdx.y;
int nbr, nn1, nn2, nn3, i, NN;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
NN = nn1*nn2*nn3;
int id = nbr + i1 + nbr*nn1 + (i3 + nbr)*nn1*nn2;
int idp = i1 + i3*nz;
if (i1 < nz && i3 < ny)
{
if (read)
{
p[id] = innerlr[idp];
p[id + (nx - 1)*nn1] = innerlr[idp + (ny*nz)];
}
else
{
innerlr[idp] = p[id];
innerlr[idp + (ny*nz)] = p[id + (nx - 1)*nn1];
}
}
}
__global__ void cuda_rw_innerfr(float *innerfr, float *p, int nz, int nx, int ny, int nb, bool read)
/*< read and write the inner computation zone boundary coefficients from and into RAM along z direction
read==flase, write/save boundary; read==true, read the boundary >*/
{
int i1 = blockIdx.x*blockDim.x + threadIdx.x;
int i2 = blockIdx.y*blockDim.y + threadIdx.y;
int nbr, nn1, nn2, nn3, i, NN;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
NN = nn1*nn2*nn3;
int id = nbr + i1 + (nbr + i2)*nn1 + nbr*nn1*nn2;
int idp = i1 + i2*nz;
if (i1 < nz && i2 < nx)
{
if (read)
{
p[id] = innerfr[idp];
p[id + (ny - 1)*nn1*nn2] = innerfr[idp + (nx*nz)];
}
else
{
innerfr[idp] = p[id];
innerfr[idp + (nx*nz)] = p[id + (ny - 1)*nn1*nn2];
}
}
}
__global__ void cuda_abc(float*abc, int nbr)
{
int id = threadIdx.x + blockDim.x*blockIdx.x;
float t = 0.015*(nbr - 1 - id);
if (id < nbr) abc[id] = expf(-t*t);
}
__global__ void cuda_set_gg(int *gzxy, int ng, int nz, int nx, int ny, int nb)
/*< set the positions of sources and geophones in whole domain >*/
{
int i2 = blockIdx.x*blockDim.x + threadIdx.x;
int i3 = blockIdx.y*blockDim.y + threadIdx.y;
int nbr, nn1, nn2, nn3, i, NN;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
NN = nn1*nn2*nn3;
int id = nbr + (i2 + nbr)*nn1 + (i3 + nbr)*nn1*nn2;
int idp = i2 + i3*nx;
if (idp<ng) gzxy[idp] = id;
}
__global__ void cuda_record(float*p, float *seis_kt, int *Gxz, int ng)
{
int id = threadIdx.x + blockDim.x*blockIdx.x;
if (id<ng) seis_kt[id] = p[Gxz[id]];
}
__global__ void cuda_cross_correlate(float *Isg, float *Iss, float *sp, float *gp, int nz, int nx, int ny, int nb)
/*< perform cross-correlation >*/
{
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
int nbr, nn1, nn2, nn3, i, NN, i3;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr)
{
float ps = sp[id];
float pg = gp[id];
Isg[id] += ps*pg;
Iss[id] += ps*ps;
}
}
}
__global__ void cuda_cross_correlate_oneinlin(float *Isg, float *Iss, float *sp, float *gp, int nz_s, int nx_s, int ny_s, int nz_g, int nx_g, int ny_g, int index_sx_xd, int index_gx_xd, int nb)
/*< perform cross-correlation >*/
{
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i3 = threadIdx.y + blockDim.y*blockIdx.y;
int nbr, nn1_s, nn2_s, nn3_s, nn1_g, nn2_g, nn3_g, i, NN_s, NN_g;
nbr = nb + radius;
nn1_s = nz_s + 2 * nbr;
nn2_s = nx_s + 2 * nbr;
nn3_s = ny_s + 2 * nbr;
nn1_g = nz_s + 2 * nbr;
nn2_g = nx_g + 2 * nbr;
nn3_g = ny_g + 2 * nbr;
int id_s = i1 + (index_sx_xd + nbr)*nn1_s + (i3)*nn1_s*nn2_s;
int id_g = i1 + (index_gx_xd + nbr)*nn1_g + (i3)*nn1_g*nn2_g;
int id = i1 + i3*nn1_s;
if (i1 >= nbr && i1 < nn1_s - nbr && i3 >= nbr && i3 < nn3_s - nbr)
{
float ps = sp[id_s];
float pg = gp[id_g];
Isg[id] += ps*pg;
Iss[id] += ps*ps;
}
}
__global__ void cuda_cross_correlate2(float *Isg, float *Iss, float *sp, float *gp, int *nsg, int *nss, int nz, int nx, int ny, int nb)
/*< perform cross-correlation >*/
{
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
int nbr, nn1, nn2, nn3, i, NN, i3;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr)
{
float ps = sp[id];
float pg = gp[id];
if (ps*pg != 0) nsg[id]++;
if (ps*ps != 0) nss[id]++;
Isg[id] += ps*pg;
Iss[id] += ps*ps;
}
}
}
__global__ void cuda_imaging(float *Isg, float *Iss, float *I1, float *I2, int nz, int nx, int ny, int nb)
/*< imaging condition with and without illumination compensation >*/
{
int nbr, nn1, nn2, nn3, i, NN, i3;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr)
{
I1[id] += Isg[id]; // correlation imaging condition
I2[id] += Isg[id] / (Iss[id] + EPS); // image normalization with illumination
}
}
}
__global__ void cuda_imaging22(float *Isg, float *Iss, float *I1, float *I2, int nz, int nx, int ny, int nb)
/*< imaging condition with and without illumination compensation >*/
{
int nbr, nn1, nn2, nn3, i, NN, i3;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr)
{
I1[id] = Isg[id]; // correlation imaging condition
I2[id] = Isg[id] / (Iss[id] + EPS); // image normalization with illumination
}
}
}
__global__ void cuda_imaging2(float *Isg, float *Iss, float *I1, float *I2, int *nsg, int *nss, int nz, int nx, int ny, int nb)
/*< imaging condition with and without illumination compensation >*/
{
int nbr, nn1, nn2, nn3, i, NN, i3;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr)
{
if (nsg[id] > 0) Isg[id] = Isg[id] / nsg[id];
if (nss[id] > 0) Iss[id] = Iss[id] / nss[id];
I1[id] += Isg[id]; // correlation imaging condition
I2[id] += Isg[id] / (Iss[id] + EPS); // image normalization with illumination
}
}
}
__global__ void cuda_imaging_oneinlin(float *Isg, float *Iss, float *I1, float *I2, int nz_s, int nx_s, int ny_s, int nz_g, int nx_g, int ny_g, int index_sx_xd, int index_gx_xd, int nb)
/*< perform cross-correlation >*/
{
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i3 = threadIdx.y + blockDim.y*blockIdx.y;
int nbr, nn1_s, nn2_s, nn3_s, nn1_g, nn2_g, nn3_g, i, NN_s, NN_g;
nbr = nb + radius;
nn1_s = nz_s + 2 * nbr;
nn2_s = nx_s + 2 * nbr;
nn3_s = ny_s + 2 * nbr;
nn1_g = nz_s + 2 * nbr;
nn2_g = nx_g + 2 * nbr;
nn3_g = ny_g + 2 * nbr;
//int id_s = i1 + (index_sx_xd + nbr)*nn1_s + (i3)*nn1_s*nn2_s;
//int id_g = i1 + (index_gx_xd + nbr)*nn1_g + (i3)*nn1_g*nn2_g;
int id = i1 + i3*nn1_s;
if (i1 >= nbr && i1 < nn1_s - nbr && i3 >= nbr && i3 < nn3_s - nbr)
{
I1[id] = Isg[id]; // correlation imaging condition
I2[id] = Isg[id] / (Iss[id] + EPS); // image normalization with illumination
}
/*
int nbr, nn1, nn2, nn3, i, NN, i3;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr)
{
I1[id] = Isg[id]; // correlation imaging condition
I2[id] = Isg[id] / (Iss[id] + EPS); // image normalization with illumination
}
}
*/
}
__global__ void cuda_taper(float *I1, float *I2, int gx, int gy, int length, int dis1, int nz, int nx, int ny, int nb)
/*< imaging condition with and without illumination compensation >*/
{
int nbr, nn1, nn2, nn3, i, NN, i3, sb;
int sx;
int sy;
float taper = 0;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
//int middle = (int)( (gx - sx)*(gx - sx));
int dis2 = 0;
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr && abs(gx - i2))
{
sx = i2 - nbr;
sy = i3;
dis2 = (int)(sqrt(1.0*((sx - gx)*(sx - gx) + (sy - gy)*(sy - gy))));
if (dis2 > length && dis2<dis1)
{
taper = 1;
}
if (dis2 <= length)
{
taper = 1;
}
if (dis2 > dis1 - length && dis2 <dis1)
{
taper = 1.0*(dis1 - dis2) / (1.0*(length - 1));
//taper =1.0;
}
if (dis2>dis1)
{
taper = 0;
}
I1[id] *= taper; // correlation imaging condition
I2[id] *= taper; // image normalization with illumination
}
}
}
__global__ void cuda_taper2(float *I1, float *I2, int gx, int gy, float length, float dis1, int nz, int nx, int ny, int nb, int flagk, float k1, int z_beg)
/*< imaging condition with and without illumination compensation >*/
{
int nbr, nn1, nn2, nn3, i, NN, i3, sb;
int sx, sy, sz;
int flag = 0;
float taper = 0;
float taper2 = 0;
float taper3 = 0;
float dis3 = 0;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
//int middle = (int)( (gx - sx)*(gx - sx));
int dis2 = 0;
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr && abs(gx - i2))
{
sx = i2 - nbr;
sy = i3;
dis2 = (sqrt(1.0*((sx - gx)*(sx - gx) + 1.0*(sy - gy)*(sy - gy))));
if (sz>z_beg + 5 && dis2 <= dis1)
{
dis3 = (1.0*(sz - z_beg) / (1.0*(2 * sz - z_beg))*dis1);
if (dis2 >= dis3)
{
//taper3 = 1.0*((dis1 - dis2)) / (1.0*(dis1 - dis3));
if (dis2 - dis3 <= length)
{
taper3 = 1.0*(length + dis3 - dis2) / (1.0*length);
taper3 = 0;
}
else
{
taper3 = 0;
}
}
else
{
taper3 = 0;
}
}
else
{
taper3 = 0;
}
if ((k1*(sx - gx) + gy*1.0) > 1.0*sy)
{
flag = 3;
}
else
{
flag = -3;
}
if (flag == flagk)
{
taper2 = 1.0;
}
dis2 = 10 * (sqrt(1.0*((sx - gx)*(sx - gx) + (sy - gy)*(sy - gy))));
if (dis2 > length && dis2<dis1)
{
taper = 1;
}
if (dis2 <= length)
{
taper = 1;
}
if (dis2 > dis1 - length && dis2 <dis1)
{
taper = 1.0*(dis1 - dis2) / (1.0*(length - 1));
//taper =1.0;
}
if (dis2>dis1)
{
taper = 0;
}
I1[id] *= taper*taper2; // correlation imaging condition
I2[id] *= taper*taper2; // image normalization with illumination
}
}
}
__global__ void cuda_taper3(float *I1, float *I2, int gx, int gy, int length, int dis1, int nz, int nx, int ny, int nb, int flagk, float k1, int z_beg)
/*< imaging condition with and without illumination compensation >*/
{
int nbr, nn1, nn2, nn3, i, NN, i3, sb;
int sx, sy, sz;
int flag = 0;
float taper = 0;
float taper2 = 0;
float taper3 = 0;
float dis3 = 0;
nbr = nb + radius;
nn1 = nz + 2 * nbr;
nn2 = nx + 2 * nbr;
nn3 = ny + 2 * nbr;
//int middle = (int)( (gx - sx)*(gx - sx));
int dis2 = 0;
int i1 = threadIdx.x + blockDim.x*blockIdx.x;
int i2 = threadIdx.y + blockDim.y*blockIdx.y;
for (i3 = 0; i3 < ny; i3++)
{
int id = i1 + i2*nn1 + (i3 + nbr)*nn1*nn2;
if (i1 >= nbr && i1<nn1 - nbr && i2 >= nbr && i2<nn2 - nbr && abs(gx - i2))
{
sx = i2 - nbr;
sy = i3;
sz = i1 - nbr;
dis2 = 10 * (sqrt(1.0*((sx - gx)*(sx - gx) + 1.0*(sy - gy)*(sy - gy))));
if (sz>z_beg + 5)
{
dis3 = (1.0*(sz - z_beg) / (1.0*(2 * sz - z_beg))*dis1);
if (dis2 >= dis3)
{
//taper3 = 1.0*((dis1 - dis2)) / (1.0*(dis1 - dis3));
if (dis2 - dis3 <= length)
{
taper3 = 1.0*(length + dis3 - dis2) / (1.0*length);
//taper3 = 0;
}
else
{
taper3 = 0;
}
}
else
{
taper3 = 1;
}
}
else