Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes

作者：HU Jiupeng,YU Ziye,KUANG Wenhuan,WANG Weitao,RUAN Xiang,DAI Shigui

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.

发文机构：Key Laboratory of Earthquake Source Physics The School of Earth Sciences and Engineering State Key Laboratory of Geodesy and Earths Dynamics Department of Geophysics Sichuan Earthquake Agency

关键词：DeepLearningPhasePickReservoirMicroseismic

分类号： P31[天文地球—固体地球物理学]