Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks,Sichuan,China

PengCheng Zhou; William L.Ellsworth; HongFeng Yang; Yen Joe Tan; Gregory C.Beroza; MinHan Sheng; RiSheng Chu

首页> 中文期刊> 《地球与行星物理:英文版》 >Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks,Sichuan,China

Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks,Sichuan,China

开具论文收录证明 >>

期刊封面封底目录下载 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences.Increasing seismicity in shale gas blocks of the Sichuan Basin,China,has presented a serious challenge to monitoring and managing the seismicity itself.In this study,to detect events we apply a machine-learning-based phase picker(PhaseNet)to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks(SGB).Both P-and S-phases are picked and associated for location.We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model.Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog.Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center(CENC),which used only the sparsely distributed permanent stations.We also measure the local magnitude and achieve magnitude completeness of ML0.We relocate clusters of events,showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks.Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network.The algorithms can facilitate rapid characterization of earthquake sequences.

著录项

来源
《地球与行星物理:英文版》 |2021年第6期|P.501-519|共19页
作者
PengCheng Zhou; William L.Ellsworth; HongFeng Yang; Yen Joe Tan; Gregory C.Beroza; MinHan Sheng; RiSheng Chu;
展开▼
作者单位

Key Laboratory of Ocean and Marginal Sea Geology South China Sea Institute of Oceanology Innovation Academy of South China Sea Ecology and Environmental Engineering Chinese Academy of Sciences Guangzhou 510301 ChinaEarth System Science Programme Faculty of Science The Chinese University of Hong Kong Sha Tin Hong Kong 999077 ChinaDepartment of Geophysics Stanford University Stanford CA 94305 USA;

Department of Geophysics Stanford University Stanford CA 94305 USA;

Earth System Science Programme Faculty of Science The Chinese University of Hong Kong Sha Tin Hong Kong 999077 China;

Earth System Science Programme Faculty of Science The Chinese University of Hong Kong Sha Tin Hong Kong 999077 ChinaDepartment of Geophysics Stanford University Stanford CA 94305 USA;

Department of Geophysics Stanford University Stanford CA 94305 USA;

State Key Laboratory of Geodesy and Earth’s Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan 430077 China;

State Key Laboratory of Geodesy and Earth’s Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan 430077 China;

展开▼
原文格式 PDF
正文语种 chi
中图分类大地（岩石界）物理学（固体地球物理学）;
关键词
induced seismicity; machine learning; Weiyuan Shale Gas Block;

Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks,Sichuan,China

摘要

著录项

相关主题

期刊订阅