IGGCAS OpenIR  > 岩石圈演化国家重点实验室
Toward improved urban earthquake monitoring through deep-learning-based noise suppression
Yang, Lei1,2; Liu, Xin1,3; Zhu, Weiqiang1; Zhao, Liang2; Beroza, Gregory C.1
2022-04-01
Source PublicationSCIENCE ADVANCES
ISSN2375-2548
Volume8Issue:15Pages:9
AbstractEarthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array. Application to the dense array data and an earthquake sequence in an urban area shows that UrbanDenoiser can increase signal quality and recover signals at an SNR level down to similar to 0 dB. Earthquake location using our denoised Long Beach data does not support the presence of mantle seismicity beneath Los Angeles but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.
DOI10.1126/sciadv.abl3564
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences)
WOS KeywordDENSE SEISMIC ARRAY ; SOUTHERN CALIFORNIA ; LONG BEACH ; FAULT ; ALGORITHM ; BENEATH ; TIME
Language英语
Funding ProjectNational Natural Science Foundation of China[41888101] ; National Natural Science Foundation of China[41625016] ; National Natural Science Foundation of China[41904060] ; US Geological Survey[G20AP00015] ; Department of Energy (Basic Energy Sciences)[DE-SC0020445]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; US Geological Survey ; US Geological Survey ; US Geological Survey ; US Geological Survey ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences) ; Department of Energy (Basic Energy Sciences)
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000786201300016
PublisherAMER ASSOC ADVANCEMENT SCIENCE
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/105147
Collection岩石圈演化国家重点实验室
Corresponding AuthorZhao, Liang; Beroza, Gregory C.
Affiliation1.Stanford Univ, Dept Geophys, Stanford, CA 94305 USA
2.Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher Evolut, Beijing, Peoples R China
3.JAMSTEC YES, Kanazawa Ku, 3173-25 Showa Machi, Yokohama, Kanagawa 2360001, Japan
First Author AffilicationInstitute of Geology and Geophysics, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Geology and Geophysics, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yang, Lei,Liu, Xin,Zhu, Weiqiang,et al. Toward improved urban earthquake monitoring through deep-learning-based noise suppression[J]. SCIENCE ADVANCES,2022,8(15):9.
APA Yang, Lei,Liu, Xin,Zhu, Weiqiang,Zhao, Liang,&Beroza, Gregory C..(2022).Toward improved urban earthquake monitoring through deep-learning-based noise suppression.SCIENCE ADVANCES,8(15),9.
MLA Yang, Lei,et al."Toward improved urban earthquake monitoring through deep-learning-based noise suppression".SCIENCE ADVANCES 8.15(2022):9.
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