Monitoring seismicity in the southern Sichuan Basin using a machine learning workflow

作     者：Kang Wang Jie Zhang Ji Zhang Zhangyu Wang Huiyu Zhu Kang Wang;Jie Zhang;Ji Zhang;Zhangyu Wang;Huiyu Zhu

作者机构：School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefei230026China 

出 版 物：《Earthquake Research Advances》 (地震研究进展（英文）)

年 卷 期：2024年第4卷第1期

页      面：59-66页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 0708[理学-地球物理学] 

基　　金：the financial support of the National Key R&D Program of China(2021YFC3000701) the China Seismic Experimental Site in Sichuan-Yunnan(CSES-SY) 

主　　题：Earthquake monitoring Machine learning Local seismicity Gaussian waveform Sparse stations 

摘      要：Monitoring seismicity in real time provides significant benefits for timely earthquake warning and *** this study,we propose an automatic workflow based on machine learning(ML)to monitor seismicity in the southern Sichuan Basin of *** workflow includes coherent event detection,phase picking,and earthquake location using three-component data from a seismic *** combining Phase Net,we develop an ML-based earthquake location model called Phase Loc,to conduct real-time monitoring of the local *** approach allows us to use synthetic samples covering the entire study area to train Phase Loc,addressing the problems of insufficient data samples,imbalanced data distribution,and unreliable labels when training with observed *** apply the trained model to observed data recorded in the southern Sichuan Basin,China,between September 2018 and March *** results show that the average differences in latitude,longitude,and depth are 5.7 km,6.1 km,and 2 km,respectively,compared to the reference *** Loc combines all available phase information to make fast and reliable predictions,even if only a few phases are detected and *** proposed workflow may help real-time seismic monitoring in other regions as well.