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  2. 地质与地球物理研究所
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  4. 中国科学院页岩气与地质工程重点实验室
Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning

文献类型：期刊论文

作者Shao, Jie1,2,3; Wang, Yibo1,2,3; Yao, Yi1,2,3; Wu, Shaojiang1,2,3; Xue, Qingfeng1,2,3; Chang, Xu1,2,3
刊名GEOPHYSICS
出版日期2019-09-01
卷号84期号:5页码:KS155-KS172
ISSN号0016-8033
DOI10.1190/GEO2018-0512.1
英文摘要Microseismic 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.
WOS关键词RECONSTRUCTION ; PRESSURE ; NOISE ; DECOMPOSITION ; ALGORITHM
资助项目National Basic Research Program of China (973 Program)[2015CB258500]
WOS研究方向Geochemistry & Geophysics
语种英语
出版者SOC EXPLORATION GEOPHYSICISTS
WOS记录号WOS:000490236900026
资助机构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) ; National Basic Research Program of China (973 Program)
源URL[http://ir.iggcas.ac.cn/handle/132A11/93926]  
专题地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室
通讯作者Wang, Yibo
作者单位1.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：地质与地球物理研究所

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