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  2. 地质与地球物理研究所
  3. 中国科学院地质与地球物理研究所
  4. 岩石圈演化国家重点实验室
Toward improved urban earthquake monitoring through deep-learning-based noise suppression

文献类型：期刊论文

作者Yang, Lei1,2; Liu, Xin1,3; Zhu, Weiqiang1; Zhao, Liang2; Beroza, Gregory C.1
刊名SCIENCE ADVANCES
出版日期2022-04-01
卷号8期号:15页码:9
ISSN号2375-2548
DOI10.1126/sciadv.abl3564
英文摘要Earthquake 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.
WOS关键词DENSE SEISMIC ARRAY ; SOUTHERN CALIFORNIA ; LONG BEACH ; FAULT ; ALGORITHM ; BENEATH ; TIME
资助项目National 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]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000786201300016
出版者AMER ASSOC ADVANCEMENT SCIENCE
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 ; 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)
源URL[http://ir.iggcas.ac.cn/handle/132A11/105147]  
专题地质与地球物理研究所_岩石圈演化国家重点实验室
通讯作者Zhao, Liang; Beroza, Gregory C.
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：地质与地球物理研究所

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