Open AccessMachine‐learning‐facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks, Sichuan, China
Author(s) -
Zhou PengCheng,
Ellsworth William L.,
Yang HongFeng,
Tan Yen Joe,
Beroza Gregory C.,
Sheng MinHan,
Chu RiSheng
Publication year - 2021
Publication title -
earth and planetary physics
Language(s) - English
Resource type - Journals
ISSN - 2096-3955
DOI - 10.26464/epp2021053
Subject(s) - induced seismicity , seismology , geology , sichuan basin , magnitude (astronomy) , shale gas , hazard , seismic hazard , oil shale , paleontology , chemistry , physics , geochemistry , organic chemistry , astronomy
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 M L 0. 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.