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Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning
Shao, Jie1,2,3; Wang, Yibo1,2,3; Yao, Yi1,2,3; Wu, Shaojiang1,2,3; Xue, Qingfeng1,2,3; Chang, Xu1,2,3
2019-09-01
Source PublicationGEOPHYSICS
ISSN0016-8033
Volume84Issue:5Pages:KS155-KS172
AbstractMicroseismic data usually have a low signal-to-noise ratio, necessitating the application of an effective denoising method. Most conventional denoising methods treat each component of multicomponent data separately, e.g., denoising methods with sparse representation. However, microseismic data are often acquired with a 3C receiver, especially in borehole monitoring cases. Independent denoising ignores the relative amplitudes and vector relationships between different components. We have developed a new simultaneous denoising method for 3C microseismic data based on joint sparse representation. The three components are represented by different dictionary atoms; the dictionary can be fixed or adaptive depending on the dictionary learning method that is used. Our method adds an extra time consistency constraint with simultaneous transformation of 3C data. The joint sparse optimization problem is solved using the extended orthogonal matching pursuit. Synthetic microseismic data with a double-couple source mechanism and two field downhole microseismic data were used for testing. Independent denoising of 1C data with the fixed dictionary method and simultaneous denoising of 3C data with the fixed dictionary and dictionary learning (3C-DL) methods were compared. The results indicate that among the three methods, the 3C-DL method is the most effective in suppressing random noise, preserving weak signals, and restoring polarization information; this is achieved by combining the time consistency constraint and dictionary learning.
DOI10.1190/GEO2018-0512.1
Funding OrganizationNational Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program)
WOS KeywordRECONSTRUCTION ; PRESSURE ; NOISE ; DECOMPOSITION ; ALGORITHM
Language英语
Funding ProjectNational Basic Research Program of China (973 Program)[2015CB258500]
Funding OrganizationNational Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program) ; National Basic Research Program of China (973 Program)
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000490236900026
PublisherSOC EXPLORATION GEOPHYSICISTS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/93926
Collection页岩气与地质工程院重点实验室
Corresponding AuthorWang, Yibo
Affiliation1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
First Author AffilicationKey Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences;  Institute of Geology and Geophysics, Chinese Academy of Sciences
Corresponding Author AffilicationKey Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences;  Institute of Geology and Geophysics, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shao, Jie,Wang, Yibo,Yao, Yi,et al. Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning[J]. GEOPHYSICS,2019,84(5):KS155-KS172.
APA Shao, Jie,Wang, Yibo,Yao, Yi,Wu, Shaojiang,Xue, Qingfeng,&Chang, Xu.(2019).Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning.GEOPHYSICS,84(5),KS155-KS172.
MLA Shao, Jie,et al."Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning".GEOPHYSICS 84.5(2019):KS155-KS172.
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