Earthquake detection in the Jiangsu region, China using graphics-processing-unit-based Match & Locate and rapid earthquake association and location

作     者：Yafen Huang Shengzhong Zhang Yuejun Lv Yanzhen Li Yuting Zhang Min Liu Yafen Huang;Shengzhong Zhang;Yuejun Lv;Yanzhen Li;Yuting Zhang;Min Liu

作者机构：School of Geophysics and Information TechnologyChina University of GeosciencesBeijing 100083China Shanghai Sheshan National Geophysical ObservatoryShanghai201602China Information Network CenterChina University of GeosciencesBeijing 100083China Institute of Crustal DynamicsChina Earthquake AdministrationBeijing 100085China 

出 版 物：《Earthquake Science》 (地震学报（英文版）)

年 卷 期：2020年第33卷第1期

页      面：23-33页

学科分类：0709[理学-地质学] 0819[工学-矿业工程] 07[理学] 070801[理学-固体地球物理学] 0707[理学-海洋科学] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0815[工学-水利工程] 0816[工学-测绘科学与技术] 0813[工学-建筑学] 0825[工学-航空宇航科学与技术] 0704[理学-天文学] 0814[工学-土木工程] 

基　　金：This research is co-supported by National Key R&D Program of China(No.2017YFC1500402) National Natural Science Foundation of China(Nos.41874063 and U1939203) Shanghai Sheshan National Geophysical Observatory(No.2020K02) 

主　　题：earthquake detection rapid earthquake association and location graphics-processing-unit-based Match and Locate 

摘      要：Earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based *** evaluate the ability of two different methods,a graphicsprocessing-unit-based Match&Locate(GPU-M&L)method and a rapid earthquake association and location(REAL)method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for ***-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time ***-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&*** number of newly detected and located events is about 2.8 times more than those listed in the local *** both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a shortterm/long-term average(STA/LTA)trigger algorithm for seismic phase detection and picking by applying the *** then refine seismic locations using a least-squares location method(VELEST)and a high-precision relative location method(hypoDD).By applying STA/LTA and PhaseNet,1006 and 1893 events are associated and located,*** newly detected events are mainly clustered and show steeply dipping fault *** analyzing the performance of these methods based on long-term continuous seismic data,the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8,respectively,which are smaller than 2.6 given by the local *** REAL provides improvement compared with GPU-M&L,REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio(SNR).Stations at southeast of the study region with low SNR may lead to few detections in the same area.