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 seismi...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.展开更多
In this article,we relocated the seismic source location of the earthquakes in the Muli area of Sichuan,inverted the focal mechanism of the larger earthquakes and analyzed the relationship between the water level of t...In this article,we relocated the seismic source location of the earthquakes in the Muli area of Sichuan,inverted the focal mechanism of the larger earthquakes and analyzed the relationship between the water level of the Jinping reservoir and the frequency of the earthquake swarm. The results show that:( 1) The epicenters of the relocated small earthquake swarms are distributed in a seismic zone,and the earthquake focal depths were in the range of 0- 12 km.( 2) By analyzing the earthquake swarm spatial distribution,we found that the swarms were generated by one branch fault on the west of Xiaojinhe fault.( 3) The focal mechanism of the three earthquakes with magnitude greater than 4. 0 is significantly different,with the shallow source thrust events affected by vertical stress,and the strike-slip events are related to regional stress tectonic activity.展开更多
基金supported by the National Key R&D Program of China(2018YFC1503200)the National Natural Science Foundation of China(41790463,41804063,42074060)the Scientific Research InstitutesBasic Research and Development Operations Special Fund of the Institute of Geophysics,China Earthquake Administration(DQJB19B29,DQJB20B27)。
文摘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.
基金sponsored by the Regular Project of Earthquake Monitoring and Prediction in 2016(16C23ZX327)
文摘In this article,we relocated the seismic source location of the earthquakes in the Muli area of Sichuan,inverted the focal mechanism of the larger earthquakes and analyzed the relationship between the water level of the Jinping reservoir and the frequency of the earthquake swarm. The results show that:( 1) The epicenters of the relocated small earthquake swarms are distributed in a seismic zone,and the earthquake focal depths were in the range of 0- 12 km.( 2) By analyzing the earthquake swarm spatial distribution,we found that the swarms were generated by one branch fault on the west of Xiaojinhe fault.( 3) The focal mechanism of the three earthquakes with magnitude greater than 4. 0 is significantly different,with the shallow source thrust events affected by vertical stress,and the strike-slip events are related to regional stress tectonic activity.
