Accelerating 3D Fourier migration with graphics processing units

Computational cost is a major factor that inhibits the practicalapplicationof3Ddepthmigration.Wehavedevelopeda fastparallelschemetospeedup3Dwave-equationdepthmigration on a parallel computing device, i.e., on graphics processing units GPUs. The third-order optimized generalized-screen propagator is used to take advantage of the builtinsoftwareimplementationofthefastFouriertransform.The propagator is coded as a sequence of kernels that can be called from the computer host for each frequency component. Moving the wavefield extrapolation for each depth leveltotheGPUsallowshandlingalarge3Dvelocitymodel,but this scheme can be speeded up to a limited degree over the CPU implementation because of the low-bandwidth data transfer between host and device. We have created further speedupinthisextrapolationschemebyminimizingthelowbandwidth data transfer, which is done by storing the 3D velocity model and imaged data in the device memory, and reducing half the memory demand by compressing the 3D velocity model and imaged data using integer arrays instead of float arrays. By incorporating a 2D tapered function, timeshiftpropagator,andscalingoftheinverseFouriertransform intoacompactkernel,thecomputationtimeisreducedgreatly. Three-dimensional impulse responses and synthetic data exampleshavedemonstratedthattheGPU-basedFouriermigration typically is 25 to 40 times faster than the CPU-based implementation. It enables us to image complex media using 3D depth migration with little concern for computational cost.Themacrovelocitymodelcanbebuiltinamuchshorter turnaroundtime.