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您的检索式：作者名="DAI Shigui"
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| 1 | Preliminary results on a near-real-time rock slope damage monitoring system based on relative velocity changes following the September 5,2022 M_(S) 6.8 Luding,China earthquake显示文摘Relative seismic velocity change(dv/v)is important for monitoring changes in subsurface material properties and evaluating earthquake-induced rock slope damage in a geological disaster-prone region.In this paper,we present a rapid damage assessment on three slow-moving rock slopes by measuring dv/v decrease caused by the 2022 M_(S) 6.8 Luding earthquake in Southwest China.By applying the stretching method to the cross-correlated seismic wavefields between sensors installed on each slope,we obtain earthquake-induced dv/v decreases of~2.1%,~0.5%,and~0.2%on three slopes at distances ranging from~86 to~370 km to the epicenter,respectively.Moreover,based on seismic data recorded by 16 sensors deployed on the rock slope at a distance of~370 km away from the epicenter,a localized dv/v decease region was observed at the crest of the slope by calculating the spatial dv/v images before and after the earthquake.We also derive an empirical in situ stress sensitivity of -7.29×10^(-8)/Pa by relating the dv/v change to the measured peak dynamic stresses.Our results indicate that a rapid dv/v assessment not only can help facilitate on-site emergency response to earthquakeinduced secondary geological disasters but also can provide a better understanding of the subsurface geological risks under diverse seismic loadings. | Fan Xie Chen Liang Shigui Dai Bo Shao Huibao Huang Jinhui Ouyang Li Li Eric Larose | 2023 | Earthquake Research Advances2023，3，1： | 1 |
| 2 | Research on the Activity Characteristics of Small Earthquake Swarms in the Muli Area of Sichuan Province显示文摘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. | Cai Yichuan Dai Shigui Wei Yaling | 2016 | Earthquake Research in China2016，30，2： | 0 |
| 3 | Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes显示文摘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. | HU Jiupeng YU Ziye KUANG Wenhuan WANG Weitao RUAN Xiang DAI Shigui | 2020 | Earthquake Research in China2020，34，3： | 0 |
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