Abstract
This article presents a parallel algorithm for seismic imaging based on depth wavefield extrapolation (the solution of a one-way wave equation [OWE]) employing the pseudospectral method. The algorithm is essentially oriented on common-offset vector (COV) gathers seismic migration based on an OWE, includes several parallelism levels, and utilizes message passing interface (MPI), Nested OpenMP, and CUDA technologies. The uppermost level of parallelism involves data splitting to ensure that each dataset can be processed independently to construct parts of the COV images. The algorithm is embarrassingly parallel at this level. Next, each independent run processes several datasets to compute COV images. Each MPI process computes all COV images for a single dataset, then MPI processes exchange data to build up a single COV image for all datasets at a single node. Computation of the COV image at a node requires OpenMP parallelization so that one thread governs the GPU-based calculations and facilitates the construction of images within a thick slab. All other threads perform image interpolation within the slab. Finally, CUDA technology is used for the most computationally intense part of the algorithm—wavefield extrapolation. Such a complex algorithm structure makes it possible to process all COV images for full-azimuth seismic data employing OWE-based migration.
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