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Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep

文献类型：期刊论文

作者Wu, Xueshan1,2,3; Huang, Song1,2; Xiao, Zhuowei2,3,4; Wang, Yuan1,2
刊名FRONTIERS IN EARTH SCIENCE
出版日期2022-02-07
卷号10页码:9
关键词deep learning local earthquake catalog submarine earthquake Challenger Deep seismicity
DOI10.3389/feart.2022.817551
英文摘要Submarine active faults and earthquakes, which contain crucial information to seafloor tectonics and submarine geohazards, can be effectively characterized by precise submarine earthquake catalogs. However, the precise and rapid building of submarine earthquake catalogs is challenging due to the following facts: (i) intense noise in ocean seismic data; (ii) the sparse seismic network; (iii) the lack of historical near-field observations. In this paper, we built a deep-learning-based automatic workflow named ESPRH for automatically building submarine earthquake catalogs from continuous seismograms. The ESPRH workflow integrates Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) for initial earthquake detection and phase picking, PickNet for phase refinement, REAL for earthquake association and rough location, and HypoInverse, HypoDD for precise earthquake relocation. We apply ESPRH to the continuous data recorded by an array of 12 broadband Ocean Bottom Seismographs (OBS) near the Challenger Deep at the southern-most Mariana subduction zone from Dec. 2016 to Jun. 2017. In this study, we acquire a high-resolution local earthquakes catalog that provides new insights into the geometry of shallow fault zones. We report the active submarine faults by seismicity in Challenger Deep which is the deepest place on Earth. These faults are a significant reference for submarine geological hazards and evidence for serpentinization. Hence, the ESPRH is qualified to construct comprehensive local submarine earthquake catalogs automatically, rapidly, and precisely from raw OBS seismic data.
WOS关键词PHASE PICKING
资助项目National Natural Science Foundation of China[91858212] ; National Natural Science Foundation of China[91858214] ; National Key R&D Program of China[2018YFC0604004]
WOS研究方向Geology
语种英语
WOS记录号WOS:000760452000001
出版者FRONTIERS MEDIA SA
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 ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Key R&D Program of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/104952]  
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
地质与地球物理研究所_中国科学院矿产资源研究重点实验室
通讯作者Huang, Song
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing, Peoples R China
2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Xueshan,Huang, Song,Xiao, Zhuowei,et al. Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep[J]. FRONTIERS IN EARTH SCIENCE,2022,10:9.
APA Wu, Xueshan,Huang, Song,Xiao, Zhuowei,&Wang, Yuan.(2022).Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep.FRONTIERS IN EARTH SCIENCE,10,9.
MLA Wu, Xueshan,et al."Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep".FRONTIERS IN EARTH SCIENCE 10(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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