Research on technology and system of tunnel microseismic monitoring and rockburst early warning based on deep learning
LI Tianbin1，2，XU Weihao1，2，MA Chunchi1，2，ZHANG hang3，ZHANG Yuxuan1，2，DAI Kunkun1，2
(1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection，Chengdu University of Technology，Chengdu，Sichuan 610059，China；2. College of Environment and Civil Engineering，Chengdu University of Technology，Chengdu，
Sichuan 610059，China；3. Chongqing City Construction Investment(Group) Co.，Ltd.，Chongqing 400023，China)
Abstract：This paper relies on microseismic monitoring，deep learning，and virtual simulation technology to establish a system and platform for the automatic integrated processing of tunnel microseismic information and intelligent warning of rock bursts. Among them，this article proposes a microseismic multi-classification model based on bimodal feature extraction，establishes a dual-task model of noise reduction and arrival pickup of a waveform based on deep convolutional encoding and decoding network，and proposes a microseismic positioning algorithm based on the gravity search method. Realized automatic，efficient，and accurate processing of tunnel microseismic classification，noise reduction，picking，positioning，and source parameter calculation. Selecting cumulative apparent volume and energy index source parameters as key indicators，then establishing a parallel sequence prediction model for microseismic parameters and a prediction and warning model for rock burst incubation stage based on LSTM multi-variant network，which achieves early warning of the current future state and time evolution of rock bursts. Meanwhile，this paper achieves the integration and display of tunnel site geographic information，geological models，tunnel models，and disaster(microseismic) information based on the three-dimensional visualization framework Cesium，forming a tunnel microseismic monitoring and rock burst warning system that integrates microseismic information collection module，microseismic information cloud processing module，and rock burst prediction and warning module. The system was applied to the rock burst disaster section of the Daxiagu Tunnel of Ehan Expressway，achieving automatic，efficient，and accurate processing of massive microseismic data，verifying the effectiveness of the automatic integrated processing of tunnel microseismic information and the intelligent warning technology system for rock bursts.
李天斌1，2，许韦豪1，2，马春驰1，2，张 航3，张彧轩1，2，代坤坤1，2. 基于深度学习的隧道微震监测及岩爆预警技术与系统研究[J]. 岩石力学与工程学报, 0, (): 823-823.
LI Tianbin1，2，XU Weihao1，2，MA Chunchi1，2，ZHANG hang3，ZHANG Yuxuan1，2，DAI Kunkun1，2. Research on technology and system of tunnel microseismic monitoring and rockburst early warning based on deep learning. , 0, (): 823-823.
