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Estimating Primaries by Sparse Inversion with Cost-Effective Computation

文献类型：期刊论文

作者Zhou, Xiaopeng1,3; Liu, Yike3; Bai, Lanshu2
刊名COMMUNICATIONS IN COMPUTATIONAL PHYSICS
出版日期2020-07-01
卷号28期号:1页码:477-497
关键词Inverse problem multiple removal primary estimation impulse response
ISSN号1815-2406
DOI10.4208/cicp.OA-2018-0065
英文摘要Recently, attenuation of surface-related multiples is implemented by a large-scale sparsity-promoting inversion where the primaries are iteratively estimated without a subtraction process, which is called estimation of primaries by sparse inversion (EPSI). By inverting for surface-free impulse responses, EPSI simultaneously updates the primaries and multiples, both of which contribute to explaining the input data, and therefore promote the global convergence gradually. However, one of the major concerns of EPSI may lie in its high computational cost. In this paper, based on the same gradient-descent framework with EPSI, we develop a computationally cost-effective primary estimation approach in which a newly defined parameterization of primary-multiple model is adopted and an efficiently defined analytical step-length is developed. The developed approach can yield a better primary estimation at less computational cost as compared to EPSI, which is verified by two synthetic datasets in numerical examples. Moreover, we apply this approach to a shallow-water field dataset and achieve a desirable performance.
WOS关键词REVERSE TIME MIGRATION ; MULTIPLES ; SCATTERING
资助项目National Natural Science Foundation of China[41730425] ; National Natural Science Foundation of China[41430321] ; National Oil and Gas Major Project of China[2017ZX05008-007]
WOS研究方向Physics
语种英语
WOS记录号WOS:000532318100024
出版者GLOBAL SCIENCE PRESS
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China ; National Oil and Gas Major Project of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/96742]  
专题地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者Liu, Yike
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.China Earthquake Networks Ctr, Beijing 100045, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resource Res, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Xiaopeng,Liu, Yike,Bai, Lanshu. Estimating Primaries by Sparse Inversion with Cost-Effective Computation[J]. COMMUNICATIONS IN COMPUTATIONAL PHYSICS,2020,28(1):477-497.
APA Zhou, Xiaopeng,Liu, Yike,&Bai, Lanshu.(2020).Estimating Primaries by Sparse Inversion with Cost-Effective Computation.COMMUNICATIONS IN COMPUTATIONAL PHYSICS,28(1),477-497.
MLA Zhou, Xiaopeng,et al."Estimating Primaries by Sparse Inversion with Cost-Effective Computation".COMMUNICATIONS IN COMPUTATIONAL PHYSICS 28.1(2020):477-497.

入库方式： OAI收割

来源：地质与地球物理研究所

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