Quick Phase Identification for Dense Seismic Array with Aid from Long Term Phase Records of Co-located Sparse Permanent Stations

• 作者: CHEN Yini LI Danning LI Jun WANG Weitao WANG Zhenjie YU Junyi YU Tiehong ZHAO Mengqi
• 来源期刊: 中国地震研究（英文版）
• 年，卷（期）: 2020,（3）
• 所属期刊栏目: Special Section on AI Seismology
• 页码范围: 328-342
• 页数: 15页
• 语种: 英文
• DOI: 10.19743/j.cnki.0891-4176.202003008

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 1768 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, 718573 P picks and 512035 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.