Calculation of Surface Offset Gathers Based on Reverse Time Migration and Its Parallel Computation with Multi-GPUs

Abstract

As an important method for seismic data processing, reverse time migration (RTM) has high precision but involves high-intensity calculations. The calculation an RTM surface offset (shot–receiver distance) domain gathers provides intermediary data for an iterative calculation of migration and its velocity building. How to generate such data efficiently is of great significance to the industrial application of RTM. We propose a method for the calculation of surface offset gathers (SOGs) based on attribute migration, wherein, using migration calculations performed twice, the attribute profile of the surface offsets can be obtained, thus the image results can be sorted into offset gathers. Aiming at the problem of high-intensity computations required for RTM, we put forth a multi-graphic processing unit (GPU) calculative strategy, i.e., by distributing image computational domains to different GPUs for computation and by using the method of multi-stream calculations to conceal data transmission between GPUs. Ultimately, the computing original efficiency was higher relative to a single GPU, and more GPUs were used linearly. The test with a model showed that the attributive migration methods can correctly output SOGs, while the GPU parallel computation can effectively improve the computing efficiency. Therefore, it is of practical importance for this method to be expanded and applied in industries.

Benchmarking GPU Tensor Cores on General Matrix Multiplication Kernels through CUTLASS

“…The last decade has witnessed GPUs acting as a rising star in a myriad of domains, including scientific computing, big data analysis and machine learning. Programmers writing such workloads tend to offload performance-critical calculations to the GPUs [1][2][3] while leaving the CPUs only for control flow and inter-process communication management. These operations include general matrix multiplication (GEMM) and Convolution (Conv); both take a large portion of the layers in recent eye-catching big data and deep learning applications.…”