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Deep Learning for Picking Seismic Arrival Times

文献类型：期刊论文

作者Wang, Jian1,2,3; Xiao, Zhuowei1,4; Liu, Chang4; Zhao, Dapeng5; Yao, Zhenxing1,2
刊名JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
出版日期2019-07-01
卷号124期号:7页码:6612-6624
关键词deep learning seismic tomography arrival times
ISSN号2169-9313
DOI10.1029/2019JB017536
英文摘要Arrival times of seismic phases contribute substantially to the study of the inner working of the Earth. Despite great advances in seismic data collection, the usage of seismic arrival times is still insufficient because of the overload manual picking tasks for human experts. In this work we employ a deep-learning method (PickNet) to automatically pick much more P and S wave arrival times of local earthquakes with a picking accuracy close to that by human experts, which can be used directly to determine seismic tomography. A large number of high-quality seismic arrival times obtained with the deep-learning model may contribute greatly to improve our understanding of the Earth's interior structure. Plain Language Summary Deep learning is currently attracting immense research interest in seismology due to its powerful ability to deal with huge seismic data collections. In this study we developed a deep-learning model (PickNet) that can rapidly pick a great number of first P and S wave arrival times precisely from local earthquake seismograms. The picking accuracy of the arrival times provided by our PickNet model is close to that by human experts. The data are good enough to be used directly to determine high-resolution 3-D velocity models of the Earth. Our PickNet model can deal with seismic waveforms provided by data centers of different earthquake networks. Furthermore, our PickNet model is also a potential tool for automatically picking later seismic phases accurately. A large number of high-quality seismic arrival times can be used to illuminate the Earth structure clearly. Hence, this study may greatly contribute to improve our knowledge of the Earth's interior.
WOS关键词P-PHASE ; AUTOMATIC PICKING ; NETWORK ; JAPAN ; MICROEARTHQUAKES ; EARTHQUAKES ; RELOCATION ; OBSPY
资助项目National Key R&D Program of China[2017YFC0601206] ; National Natural Science Foundation of China[41474043] ; National Natural Science Foundation of China[41274089] ; Youth Innovation Promotion Association of CAS[2014058]
WOS研究方向Geochemistry & Geophysics
语种英语
WOS记录号WOS:000481819500024
出版者AMER GEOPHYSICAL UNION
资助机构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 ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; 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 ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; 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 ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; 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 ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS
源URL[http://ir.iggcas.ac.cn/handle/132A11/93352]  
专题地质与地球物理研究所_中国科学院地球与行星物理重点实验室
通讯作者Wang, Jian; Xiao, Zhuowei
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Earth Sci, Beijing, Peoples R China
3.Qingdao Natl Lab Marine Sci & Technol, Lab Marine Mineral Resources, Qingdao, Shandong, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Tohoku Univ, Grad Sch Sci, Dept Geophys, Sendai, Miyagi, Japan
推荐引用方式
GB/T 7714		 Wang, Jian,Xiao, Zhuowei,Liu, Chang,et al. Deep Learning for Picking Seismic Arrival Times[J]. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,2019,124(7):6612-6624.
APA Wang, Jian,Xiao, Zhuowei,Liu, Chang,Zhao, Dapeng,&Yao, Zhenxing.(2019).Deep Learning for Picking Seismic Arrival Times.JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,124(7),6612-6624.
MLA Wang, Jian,et al."Deep Learning for Picking Seismic Arrival Times".JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 124.7(2019):6612-6624.

入库方式： OAI收割

来源：地质与地球物理研究所

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