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Seismic detection method for small-scale discontinuities based on dictionary learning and sparse representation

文献类型：期刊论文

作者Yu, Caixia1; Zhao, Jingtao2; Wang, Yanfei1
刊名JOURNAL OF APPLIED GEOPHYSICS
出版日期2017-02-01
卷号137页码:55-62
关键词Small-scale Discontinuities Sparse Representation Dictionary Learning Orthogonal Matching Pursuit
ISSN号0926-9851
DOI10.1016/j.jappgeo.2016.12.005
文献子类Article
英文摘要Studying small-scale geologic discontinuities, such as faults, cavities and fractures, plays a vital role in analyzing the inner conditions of reservoirs, as these geologic structures and elements can provide storage spaces and migration pathways for petroleum. However, these geologic discontinuities have weak energy and are easily contaminated with noises, and therefore effectively extracting them from seismic data becomes a challenging problem. In this paper, a method for detecting small-scale discontinuities using dictionary learning and sparse representation is proposed that can dig up high-resolution information by sparse coding. A K-SVD (K-means clustering via Singular Value Decomposition) sparse representation model that contains two stage of iteration procedure: sparse coding and dictionary updating, is suggested for mathematically expressing these seismic small-scale discontinuities. Generally, the orthogonal matching pursuit (OMP) algorithm is employed for sparse coding. However, the method can only update one dictionary atom at one time. In order to improve calculation efficiency, a regularized version of OMP algorithm is presented for simultaneously updating a number of atoms at one time. Two numerical experiments demonstrate the validity of the developed method for clarifying and enhancing small-scale discontinuities. The field example of carbonate reservoirs further demonstrates its effectiveness in revealing masked tiny faults and small-scale cavities. (C) 2016 Elsevier B.V. All rights reserved.
WOS关键词FILTERS
WOS研究方向Geology ; Mining & Mineral Processing
语种英语
WOS记录号WOS:000395841400006
出版者ELSEVIER SCIENCE BV
资助机构National Natural Science Foundation of China(41325016 ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB10020101) ; Chinese postdoctoral science fund(2014 M550829) ; 41604125) ; National Natural Science Foundation of China(41325016 ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB10020101) ; Chinese postdoctoral science fund(2014 M550829) ; 41604125) ; National Natural Science Foundation of China(41325016 ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB10020101) ; Chinese postdoctoral science fund(2014 M550829) ; 41604125) ; National Natural Science Foundation of China(41325016 ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB10020101) ; Chinese postdoctoral science fund(2014 M550829) ; 41604125)
源URL[http://ir.iggcas.ac.cn/handle/132A11/52996]  
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Zhao, Jingtao
作者单位1.Chinese Acad Sci, Key Lab Petr Resources Res, Inst Geol & Geophys, Beijing 100029, Peoples R China
2.China Univ Min & Technol, State Key Lab Coal Resources & Safe Min, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Caixia,Zhao, Jingtao,Wang, Yanfei. Seismic detection method for small-scale discontinuities based on dictionary learning and sparse representation[J]. JOURNAL OF APPLIED GEOPHYSICS,2017,137:55-62.
APA Yu, Caixia,Zhao, Jingtao,&Wang, Yanfei.(2017).Seismic detection method for small-scale discontinuities based on dictionary learning and sparse representation.JOURNAL OF APPLIED GEOPHYSICS,137,55-62.
MLA Yu, Caixia,et al."Seismic detection method for small-scale discontinuities based on dictionary learning and sparse representation".JOURNAL OF APPLIED GEOPHYSICS 137(2017):55-62.

入库方式： OAI收割

来源：地质与地球物理研究所

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