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基于随机采样的频率域多路径波场模拟与偏移成像

李博

石油物探2017,Vol.56Issue(3):382-389,438,9.
石油物探2017,Vol.56Issue(3):382-389,438,9.DOI:10.3969/j.issn.1000-1441.2017.03.008

基于随机采样的频率域多路径波场模拟与偏移成像

Multipath seismic simulation and imaging in frequency domain based on random sampling method

李博1

作者信息

  • 1. 中国石油化工股份有限公司石油物探技术研究院,江苏南京211103;中国科学院地质与地球物理研究所油气资源研究重点实验室,北京100029
  • 折叠

摘要

Abstract

Conventional Kirchhoff prestack depth migration cannot handle the wave fields with caustic phenomenon;consequently,the subsurface reflector imaging could be distorted.In the complex media,Kirchhoff migration based on ray tracing must be inevitably affected by the caustic problem.In this paper,a multipath wave field simulation method based on the secondary source Huygens theory in complex media is proposed.Firstly,we ensure that there is no ray-cross in the local scope for domain decomposition.Then we use the secondary source Kirchhoff-Huygens integral method to gradually complete wave field extrapolation for the entire imaging area.In this way,we can correctly handle the caustic phenomena and get the correct imaging reflector.The wave field simulation method and domain decomposition strategy of secondary sources definition we described here have included large-scale matrix calculation that is the main bottleneck of efficiency.We propose a wavefield simulation algorithm based on random sampling and low rank decomposition method for large-scale matrix multiplication.On the other hand,we use GPU as the computing devices to realize the multipath seismic simulation and imaging in frequency domain,which greatly improve the computational efficiency.The numerical simulation test results show caustic could be removed and the correct reflector imaging is realized with this method.

关键词

偏移成像/波场外推/焦散/格林函数/低秩分解/随机采样

Key words

seismic imaging/wave field extrapolation/caustic/Green function/low-rank decomposition/random sampling

分类

天文与地球科学

引用本文复制引用

李博..基于随机采样的频率域多路径波场模拟与偏移成像[J].石油物探,2017,56(3):382-389,438,9.

基金项目

国家重点研发计划项目(2016YFC0601101)资助.This research is financially supported by the Nation Key R & D Program of China (Grant No.2016YFC0601101). (2016YFC0601101)

石油物探

OA北大核心CSCDCSTPCD

1000-1441

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