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Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes
HU Jiupeng1,2, YU Ziye3, KUANG Wenhuan4, WANG Weitao1, RUAN Xiang5, DAI Shigui5
1.Key Laboratory of Earthquake Source Physics, Institute of Geophysics, China Earthquake Administration,Beijing 100081, China;2.The School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023,China;3.State Key Laboratory of Geodesy and Earths Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;4.Department of Geophysics, Stanford University, CA 94305, USA;5.Sichuan Earthquake Agency, Chengdu 610041, China
摘要:
Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.
关键词:  Deep Learning  Phase Pick  Reservoir Microseismic
DOI:10.19743/j.cnki.0891-4176.202003007
分类号:
基金项目:This research was supported by the National Key R&D Program of China (2018YFC1503200),the National Natural Science Foundation of China (41790463,41804063,42074060) and the Scientific Research Institutes' Basic Research and Development Operations Special Fund of the Institute of Geophysics, China Earthquake Administration (DQJB19B29, DQJB20B27).
Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes
HU Jiupeng1,2, YU Ziye3, KUANG Wenhuan4, WANG Weitao1, RUAN Xiang5, DAI Shigui5
1.Key Laboratory of Earthquake Source Physics, Institute of Geophysics, China Earthquake Administration,Beijing 100081, China;2.The School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023,China;3.State Key Laboratory of Geodesy and Earths Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;4.Department of Geophysics, Stanford University, CA 94305, USA;5.Sichuan Earthquake Agency, Chengdu 610041, China
Abstract:
Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.
Key words:  Deep Learning  Phase Pick  Reservoir Microseismic