High-dimension Seismic Data Regularization on CPU/GPU Heterogeneous Computing System

Seismic data is usually acquired irregularly and sparsely due to obstacles and high cost of acquisition, making the regularization of seismic data with anti-leakage and anti-aliasing techniques essential in the seismic data processing workflow. We propose a new method of 4D anti-leakage and anti-aliasing Fourier reconstruction using a cube-removal strategy to handle the combination of irregular sampling and aliasing in high-dimension seismic data. Our approach computes a weighting function by stacking the spectrum along the radial lines to suppress the aliasing energy, and then iteratively pick the largest amplitude cube to construct the Fourier spectrum. To fully leverage the power of the supercomputer, we design a multiple-level parallel architecture using CPU/GPU heterogeneous computing system. Specifically, we have developed parallel data splitting, multiple GPU devices computing, and parallel merging. The efficiency test validates that the parallel architecture can achieve a high acceleration ratio compared with CPU version and attain nearly linear performance scalability with the GPU devices number. The numerical test on one field data example demonstrates the robustness and effectiveness of our method.