As one of the most seismically active regions,Sichuan Basin is a key area of seismological studies in China.This study applies a neural network model with attention mechanisms,simultaneously picking the P-wave arrival...As one of the most seismically active regions,Sichuan Basin is a key area of seismological studies in China.This study applies a neural network model with attention mechanisms,simultaneously picking the P-wave arrival times and determining the first-motion polarity.The polarity information is subsequently used to derive source focal mechanisms.The model is trained and tested using small to moderate earthquake data from June to December 2019 in Sichuan.We apply the trained model to predict first-motion polarity directions of earthquake recordings in Sichuan from January to May 2019,and then derive focal mechanism solutions using HASH algorithm with predicted results.Compared with the source mechanism solutions obtained by manual processing,the deep learning method picks more polarities from smaller events,resulting in more focal mechanism solutions.The catalog documents focal mechanism solutions of 22 events(M_(L) 2.6–4.8)from analysts during this period,whereas we obtain focal mechanism solutions of 53 events(M_(L) 1.9–4.8)through the deep learning method.The derived focal mechanism solutions for the same events are consistent with the manual solutions.This method provides an efficient way for the source mechanism inversion of small to moderate earthquakes in Sichuan region,with high stability and reliability.展开更多
High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology.Here,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deplo...High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology.Here,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deployed in the southern north-south seismic zone(20°N-32°N,95°E-110°E)in 2011-2013 using 355 portable broadband seismic stations.CREDIT-X1local,the first release of the ChinArray Reference Earthquake Dataset for Innovative Techniques(CREDIT),includes comprehensive information for the 105,455 local events that occurred in the southern north-south seismic zone during array observation,incorporating them into a single HDF5 file.Original 100-Hz sampled three-component waveforms are organized by event for stations within epicenter distances of 1,000 km,and records of≥200 s are included for each waveform.Two types of phase labels are provided.The first includes manually picked labels for 5,999 events with magnitudes≥2.0,providing 66,507 Pg,42,310 Sg,12,823 Pn,and 546 Sn phases.The second contains automatically labeled phases for 105,442 events with magnitudes of−1.6 to 7.6.These phases were picked using a recurrent neural network phase picker and screened using the corresponding travel time curves,resulting in 1,179,808 Pg,884,281 Sg,176,089 Pn,and 22,986 Sn phases.Additionally,first-motion polarities are included for 31,273 Pg phases.The event and station locations are provided,so that deep learning networks for both conventional phase picking and phase association can be trained and validated.The CREDIT-X1local dataset is the first million-scale dataset constructed from a dense seismic array,which is designed to support various multi-station deep-learning methods,high-precision focal mechanism inversion,and seismic tomography studies.Additionally,owing to the high seismicity in the southern north-south seismic zone in China,this dataset has great potential for future scientific discoveries.展开更多
基金the National Key R&D Program of China(2021YFC3000701)for the financial support。
文摘As one of the most seismically active regions,Sichuan Basin is a key area of seismological studies in China.This study applies a neural network model with attention mechanisms,simultaneously picking the P-wave arrival times and determining the first-motion polarity.The polarity information is subsequently used to derive source focal mechanisms.The model is trained and tested using small to moderate earthquake data from June to December 2019 in Sichuan.We apply the trained model to predict first-motion polarity directions of earthquake recordings in Sichuan from January to May 2019,and then derive focal mechanism solutions using HASH algorithm with predicted results.Compared with the source mechanism solutions obtained by manual processing,the deep learning method picks more polarities from smaller events,resulting in more focal mechanism solutions.The catalog documents focal mechanism solutions of 22 events(M_(L) 2.6–4.8)from analysts during this period,whereas we obtain focal mechanism solutions of 53 events(M_(L) 1.9–4.8)through the deep learning method.The derived focal mechanism solutions for the same events are consistent with the manual solutions.This method provides an efficient way for the source mechanism inversion of small to moderate earthquakes in Sichuan region,with high stability and reliability.
基金funded by the National Key R&D Program of China (No. 2021YFC3000702)the Special Fund of the Institute of Geophysics, China Earthquake Administration (No. DQJB20B15)+2 种基金the National Natural Science Foundation of China youth Grant (No. 41804059)the Joint Funds of the National Natural Science Foundation of China (No. U223920029)the Science for Earthquake Resilience of China Earthquake Administration (No. XH211103)
文摘High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology.Here,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deployed in the southern north-south seismic zone(20°N-32°N,95°E-110°E)in 2011-2013 using 355 portable broadband seismic stations.CREDIT-X1local,the first release of the ChinArray Reference Earthquake Dataset for Innovative Techniques(CREDIT),includes comprehensive information for the 105,455 local events that occurred in the southern north-south seismic zone during array observation,incorporating them into a single HDF5 file.Original 100-Hz sampled three-component waveforms are organized by event for stations within epicenter distances of 1,000 km,and records of≥200 s are included for each waveform.Two types of phase labels are provided.The first includes manually picked labels for 5,999 events with magnitudes≥2.0,providing 66,507 Pg,42,310 Sg,12,823 Pn,and 546 Sn phases.The second contains automatically labeled phases for 105,442 events with magnitudes of−1.6 to 7.6.These phases were picked using a recurrent neural network phase picker and screened using the corresponding travel time curves,resulting in 1,179,808 Pg,884,281 Sg,176,089 Pn,and 22,986 Sn phases.Additionally,first-motion polarities are included for 31,273 Pg phases.The event and station locations are provided,so that deep learning networks for both conventional phase picking and phase association can be trained and validated.The CREDIT-X1local dataset is the first million-scale dataset constructed from a dense seismic array,which is designed to support various multi-station deep-learning methods,high-precision focal mechanism inversion,and seismic tomography studies.Additionally,owing to the high seismicity in the southern north-south seismic zone in China,this dataset has great potential for future scientific discoveries.
