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Quick Phase Identification for Dense Seismic Array with Aid from Long Term Phase Records of Co-located Sparse Permanent Stations
CHEN Yini1, LI Jun1, WANG Zhenjie1, ZHAO Mengqi1, YU Tiehong1, LI Danning2, YU Junyi1, WANG Weitao3
1.Zhejiang Earthquake Agency, Hangzhou 310013, China;2.Yunnan Earthquake Agency, Kunming 650224, China;3.Key Laboratory of Earthquake Source Physics, Institute of Geophysics, China Earthquake Administration, Beijing 100086, China
摘要:
The phase identification and travel time picking are critical for seismic tomography, yet it will be challenging when the numbers of stations and earthquakes are huge. We here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent stations. The records for 1 768 M ≥ 2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectilinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718 573 P picks and 512 035 S picks are obtained from mobile stations, which are 28 and 22 times of those from permanent stations, respectively. By comparing the automatic picks with manual picks from 88 permanent stations, for M ≥ 3.0 events, 81.5% of the P-pick errors are smaller than 0.5 second and 70.5% of S-pick errors are smaller than 1 second. For events with a lower magnitude, 76.5% P-pick errors fall into 0.5 second and 69.5% S-pick errors are smaller than 1 second. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, indicating the necessity of combining traditional auto picking and integrating machine learning method.
关键词:  Phase picking  Travel time  Dense array  Spatial overlap
DOI:10.19743/j.cnki.0891-4176.202003008
分类号:
基金项目:This project is supported by the National Key Research and Development Program of China (2018YFC1503200), the Fundamental Research Funds for the Institute of Geophysics of China Earthquake Administration (DQJB19B29), the National Natural Science Foundation of China (41790463) and the Science and Technology Projects of Zhejiang Earthquake Agency (2019zjj05).
Quick Phase Identification for Dense Seismic Array with Aid from Long Term Phase Records of Co-located Sparse Permanent Stations
CHEN Yini1, LI Jun1, WANG Zhenjie1, ZHAO Mengqi1, YU Tiehong1, LI Danning2, YU Junyi1, WANG Weitao3
1.Zhejiang Earthquake Agency, Hangzhou 310013, China;2.Yunnan Earthquake Agency, Kunming 650224, China;3.Key Laboratory of Earthquake Source Physics, Institute of Geophysics, China Earthquake Administration, Beijing 100086, China
Abstract:
The phase identification and travel time picking are critical for seismic tomography, yet it will be challenging when the numbers of stations and earthquakes are huge. We here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent stations. The records for 1 768 M ≥ 2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectilinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718 573 P picks and 512 035 S picks are obtained from mobile stations, which are 28 and 22 times of those from permanent stations, respectively. By comparing the automatic picks with manual picks from 88 permanent stations, for M ≥ 3.0 events, 81.5% of the P-pick errors are smaller than 0.5 second and 70.5% of S-pick errors are smaller than 1 second. For events with a lower magnitude, 76.5% P-pick errors fall into 0.5 second and 69.5% S-pick errors are smaller than 1 second. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, indicating the necessity of combining traditional auto picking and integrating machine learning method.
Key words:  Phase picking  Travel time  Dense array  Spatial overlap