DiTing:A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

作     者：Ming Zhao Zhuowei Xiao Shi Chen Lihua Fang Ming Zhao;Zhuowei Xiao;Shi Chen;Lihua Fang

作者机构：Institute of GeophysicsChina Earthquake AdministrationBeijing 100081China Beijing Baijiatuan Earth Sciences National Observation and Research StationBeijing 100095China Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Earthquake Source PhysicsChina Earthquake AdministrationBeijing 100081China 

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

年 卷 期：2023年第36卷第2期

页      面：84-94页

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 National Natural Science Foundation of China(Nos.41804047 and 42111540260) Fundamental Research Funds of the Institute of Geophysics,China Earthquake Administration(NO.DQJB19A0114) the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(No.IGGCAS-201904) 

主　　题：artificial intelligence benchmark dataset earthquake detection seismic phase identification first-motion polarity 

摘      要：In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of *** amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology *** this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing)with the largest known total time *** were recorded using broadband and short-period *** obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity *** waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an *** three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise *** magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,*** dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion *** research will further promote the development and application of artificial intelligence in seismology.