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

Dingjin Liu,Bo Li,Guofeng Liu

doi:10.3390/app112210687

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.

Highlights

As an important branch of geophysics, seismic exploration uses seismic waves excited on the Earth’s surface from seismic sources, such as explosions, and receives seismic waves reflected on the Earth’s surface from underground to image underground structures
Based on the typical characteristics of multi-graphic processing unit (GPU) programming and high memory demand of reverse time migration (RTM) surface offset gathers (SOGs), we propose a strategy to accelerate the calculation of RTM SOGs with multi-GPUs, the typical feature beings that this strategy can conceal the time required for data transmission between GPUs, achieving an increase in computing efficiency in linear proportion to the increase in the number of GPUs
We have described an RTM method for the calculation of SOGs based on attribute migration

Summary

Introduction

As an important branch of geophysics, seismic exploration uses seismic waves excited on the Earth’s surface from seismic sources, such as explosions, and receives seismic waves reflected on the Earth’s surface from underground to image underground structures. If the weighting coefficient w(x|s, r) is the offset attribute, the value of the offset at the location of the imaging point can be calculated, outputting the SOGs. Equations (1) to (3) are simplified to the cross-correlation imaging conditions of RTM and are expressed as: I(x, s)= u(x, s, t) v(x, s, r, t)drdt,. The original data and modulated data are migrated using the same imaging parameters, respectively (e.g., Figure 1a,b) and solve the ratio of the results (Figure 2a,b) from the two migrations to obtain the value of the offset at the imaging point.The imaging result is sortedby the calculated values of the surface offsets (Figure 2c) into SOGs, and the records oFfigaullreth1.e(as)hTohte goraitghinearlssianrgelec-sahlcout rleactoerddsafnrodminthseeMrteardmfooulsliomwoidnegl atnhde(abf)othreesraesidultps rfroocmestshees to ombotadiunlatthioenSoOf oGffsseotfs.the imaging results.

Process Flow for RTM-Based Calculation to Output SOGs

Conclusions