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

doi:10.13722/j.cnki.jrme.2023.0433

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摘要针对隧道工程建设中微震监测信息处理智能化不高、隧道三维可视化信息不全和隧道岩爆灾害预警难等亟待解决的问题，采用微震监测、深度学习和虚拟仿真技术，建立隧道微震信息自动一体化处理及岩爆智能预警技术体系与系统平台。提出基于双模态特征提取的微震多分类模型，建立基于深度卷积编解码网络的波形降噪–到时拾取双任务模型以及基于引力搜索法的微震定位算法，实现隧道微震分类–降噪–拾取–定位–震源参数计算的自动、高效、准确处理；选取累积视体积和能量指数震源参数作为关键指标，建立基于LSTM多变种网络的微震参数平行序列预测模型和岩爆孕育阶段判识预警模型，实现岩爆当前–未来状态时效演化的预警。同时，基于三维可视化框架Cesium实现隧址地理信息、地质模型、隧道模型、灾害(微震)信息的集成与显示，形成微震信息采集模块、微震信息云处理模块、岩爆预测预警模块于一体的隧道微震监测与岩爆预警系统。将系统应用于峨汉高速公路大峡谷隧道岩爆灾害段，实现海量微震数据的自动、高效、准确处理，验证隧道微震信息自动一体化处理及岩爆智能预警技术体系的有效性。

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	李天斌1
	2
	许韦豪1
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	马春驰1
	2
	张航3
	张彧轩1
	2
	代坤坤1
	2

关键词 ：岩石力学, 隧道, 微震监测, 岩爆预警, 深度学习, 虚拟仿真

Abstract：Relying on microseismic monitoring，deep learning and virtual simulation technology，a system and platform for the automatic integrated processing of tunnel microseismic information and intelligent warning of rock bursts is established in this paper. Both a microseismic multi-classification model based on bimodal feature extraction，and a dual-task model of noise reduction and arrival pickup of a waveform based on deep convolutional encoding and decoding network are proposed，and a microseismic positioning algorithm based on the gravity search method is put forward，for realizing 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，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 are established，which achieves early warning of the current future state and time evolution of rock bursts. Meanwhile，the integration and display of tunnel site geographic information，geological models，tunnel models and disaster(microseismic) information are achieved 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 is applied to the rock burst disaster section of the Daxiagu Tunnel of Ehan Expressway，achieving automatic，efficient and accurate processing of massive microseismic data，and verifying the effectiveness of the automatic integrated processing of tunnel microseismic information and the intelligent warning technology system for rock bursts.

Key words： rock mechanics tunnel microseismic monitoring rockburst warning deep learning virtual simulation

引用本文:

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, 2024, 43(5): 1041-1063.
LI Tianbin1，2，XU Weihao1，2，MA Chunchi1，2，ZHANG Hang3，ZHANG Yuxuan1，2，DAI Kunkun1，2. Research of technology and system of tunnel microseismic monitoring and rockburst early warning based on deep learning. , 2024, 43(5): 1041-1063.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2023.0433 或 http://rockmech.whrsm.ac.cn/CN/Y2024/V43/I5/1041

[4]	马克，唐春安，梁正召，等. 基于微震监测的地下水封石油洞库施工期围岩稳定性分析[J]. 岩石力学与工程学报，2016，35(7)： 1 353-1 365.(MA Ke，TANG Chunan，LIANG Zhengzhao，et al. Analysis of surrounding rock stability of underground water-sealed petroleum caverns during construction period based on microseismic monitoring[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(7)：1 353-1 365.(in Chinese))
[2]	GIBOWCIZ S J，KIJKO A. 矿山地震学引论[M]. 修济刚，徐平，杨心平，译. 北京：地震出版社，1998：1-155.(GIBOWCIZ S J，KIJKO A. An introduction to the seismology of mine[M]. Translated by XIU Jigang，XU Ping，YANG Xinping. Beijing：Earthquake Press，1998：1-155.(in Chinese))
[6]	于洋，冯夏庭，陈炳瑞，等. 深埋隧洞不同开挖方式下即时型岩爆微震信息特征及能量分形研究[J]. 岩土力学，2013，34(9)： 2 622-2 628.(YU Yang，FENG Xiating，CHEN Bingrui，et al. Research on microseismic information characteristics and energy fractals of instant rock burst in different excavation methods of deep-buried tunnel[J]. Rock and Soil Mechanics，2013，34(9)：2 622- 2 628.(in Chinese))
[8]	丰光亮，冯夏庭，陈炳瑞，等. 白鹤滩柱状节理玄武岩隧洞开挖微震活动时空演化特征[J]. 岩石力学与工程学报，2015，34(10)：1 967-1 975.(FENG Guangliang，FENG Xiating，CHEN Bingrui，et al. Spatio-temporal evolution characteristics of microseismic activity during excavation of Baihetan columnar jointed basalt tunnel[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(10)：1 967-1 975.(in Chinese))
[9]	谭双，李邵军，王雪亮，等. 深埋引水隧洞塌方孕育过程微震规律研究：以Neelum-Jhelum工程为例[J]. 岩石力学与工程学报，2018，37(增2)：4 115-4 124.(TAN Shuang，LI Shaojun，WANG Xueliang，et al. Microseismic characteristics during the collapse process of a deep-buried water diversion tunnel：A case study of the Neelum-Jhelum Project[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(Supp.2)：4 115-4 124.(in Chinese))
[1]	李天斌，孟陆波，王兰生. 高地应力隧道稳定性及岩爆、大变形灾害防治[M]. 北京：科学出版社，2016：361-498.(LI Tianbin，MENG Lubo，WANG Lansheng. Stability of high geostress tunnel and prevention of rock burst and large deformation disasters[M]. Beijing：Science Press，2016：361-498.(in Chinese))
[11]	马天辉，唐春安，唐烈先，等. 基于微震监测技术的岩爆预测机制研究[J]. 岩石力学与工程学报，2016，35(3)：470-483.(MA Tianhui，TANG Chun?an，TANG Liexian，et al. Mechanism of rock burst prediction based on microseismic monitoring technology[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(3)：470-483.(in Chinese))
[12]	周朝，尹健民，周春华，等. 考虑累积微震损伤效应的荒沟电站地下洞室群围岩稳定性分析[J]. 岩石力学与工程学报，2020，39(5)：1 011-1 022.(ZHOU Chao，YIN Jianmin，ZHOU Chunhua，et al. Stability analysis of the surrounding rock of a group of underground chambers in Huanggou Power Station considering cumulative microseismic damage effect[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(5)：1 011-1 022.(in Chinese))
[7]	张文东，马天辉，唐春安，等. 锦屏二级水电站引水隧洞岩爆特征及微震监测规律研究[J]. 岩石力学与工程学报，2014，33(2)：339-348.(ZHANG Wendong，MA Tianhui，TANG Chun?an，et al. Characteristics of rock burst and micro-seismic monitoring law in Jinping II hydropower station diversion tunnel[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(2)：339-348.(in Chinese))
[14]	陈炳瑞，冯夏庭，明华军，等. 深埋隧洞岩爆孕育规律与机制：时滞型岩爆[J]. 岩石力学与工程学报，2012，31(3)：2 561-2 569. (CHEN Bingrui， FENG Xiating，MING Huajun，et al. Rules and mechanisms of rock burst incubation in deep-buried tunnels：Delayed-type rock burs[J]. Chinese Journal of Rock Mechanics and Engineering，2012，31(3)：2 561-2 569.(in Chinese))
[16]	马春驰，李天斌，张航，等. 基于EMS微震参数的岩爆预警方法及探讨[J]. 岩土力学，2018，39(2)：765-774.(MA Chunchi，LI Tianbin，ZHANG Hang，et al. Method and discussion on rock burst early warning based on EMS microseismic parameters[J]. Rock and Soil Mechanics，2018，39(2)：765-774.(in Chinese))
[17]	魏秀琪，唐春安，张世超，等. 秦岭隧洞4#支洞微震规律与岩爆预警研究[J]. 地下空间与工程学报，2020，16(6)：1 866-1 874.(WEI Xiuqi，TANG Chun?an，ZHANG Shichao，et al. Research on microseismic law and rock burst early warning in Qinling tunnel No.4 branch[J]. Chinese Journal of Underground Space and Engineering，2020，16(6)：1 866-1 874.(in Chinese))
[19]	HANG R L，LI Z，GHAMISI P，et al. Classification of hyperspectral and LiDAr data using coupled CNNs[J]. IEEE Transactions on Geoscience and Remote Sensing，2020，58(7)：4 939-4 950.
[21]	MA C C，ZHANG H，LU X Q，et al. A novel microseismic classification model based on bimodal neurons in an artificial neural network[J]. Tunnelling and Underground Space Technology，2023，131：104791.
[22]	姜鹏，戴峰，徐奴文，等. 基于ST时频分析的地下厂房微震信号识别研究[J]. 岩石力学与工程学报，2015，34(增2)：4 071-4 079.(JIANG Peng，DAI Feng，XU Nuwen，et al. Study on identification of microseismic signals of underground workshop based on ST time-frequency analysis[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(Supp.2)：4 071-4 079.(in Chinese))
[24]	SUN H M，JIA R S，DU Q Q，et al. Cross-correlation analysis and time delay estimation of a homologous micro-seismic signal based on the Hilbert-Huang transform[J]. Computers and Geosciences，2016，91：98-104.
[27]	QI P H，ZHOU X Y，ZHENG S L，et al. Automatic modulation classification based on deep residual networks with multimodal information[J]. IEEE Transactions on Cognitive Communications and Networking，2021，7(1)：21-33.
[29]	ZHU W Q，BEROZA G C. PhaseNet：A deep-neural-network-based seismic arrival time picking method[J]. Geophysical Journal International，2019，261(1)：261-273.
[31]	赵洪宝，刘瑞，顾涛，等. 基于深度学习模式的微震信号P波自动拾取方法研究[J]. 岩石力学与工程学报，2021，40(增2)： 3 084-3 097.(ZHAO Hongbao，LIU Rui，GU Tao，et al. Study on P-wave automatic picking method of microseismic signals based on deep learning mode[J]. Chinese Journal of Rock Mechanics and Engineering，2021，40(Supp.2)：3 084-3 097.(in Chinese))
[32]	ZHANG H，MA C C，JIANG Y P，et al. Integrated processing method for microseismic signal based on deep neural network[J]. Geophysical Journal International，2021，226(3)：2 145-2 157.
[34]	吕进国，姜耀东，赵毅鑫，等. 基于稳健模拟退火-单纯形混合算法的微震定位研究[J]. 岩土力学，2013，34(8)：2 195-2 203.(LV Jinguo，JIANG Yaodong，ZHAO Yixin，et al. Study on microseismic location based on robust simulated annealing-simplex hybrid algorithm[J]. Rock and Soil Mechanics，2013，34(8)：2 195-2 203. (in Chinese))
[37]	马春驰，李天斌，张航，等. 基于EMS微震参数的岩爆预警方法及探讨[J]. 岩土力学，2018，39(2)：765-774.(MA Chunchi，LI Tianbin，ZHANG Hang，et al. Method and discussion on rock burst early warning based on EMS microseismic parameters[J]. Rock and Soil Mechanics，2018，39(2)：765-774.(in Chinese))
[39]	张航. 基于深度学习的隧道微震信号处理及岩爆智能预警研究[博士学位论文][D]. 成都：成都理工大学，2020.(ZHANG Hang. Study on tunnel microseismic signal processing and rock burst intelligent warning based on deep learning[Ph. D. Thesis][D]. Chengdu：Chengdu University of Technology，2020.(in Chinese))
[5]	MA T H，LIN D Y，TANG C A，et al. Microseismic monitoring，positioning principle，and sensor layout strategy of rock mass engineering[J]. Geofluids，2020，2020：1-20.
[15]	于群，唐春安，李连崇，等. 基于微震监测的锦屏二级水电站深埋隧洞岩爆孕育过程分析[J]. 岩石力学与工程学报，2014，36(12)：2 315-2 322.(YU Qun，TANG Chun?an，LI Lianchong，et al. Analysis of rock burst incubation process in Jingping II hydropower station deep-buried tunnel based on microseismic monitoring[J]. Chinese Journal of Rock Mechanics and Engineering，2014，36(12)：2 315-2 322.(in Chinese))
[25]	KRIZHEVSKY A，SUTSKEVER I，HINTON E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM，2017，60(6)：84-90.
[35]	陈炳瑞，冯夏庭，李庶林，等. 基于粒子群算法的岩体微震源分层定位方法[J]. 岩石力学与工程学报，2009，28(4)：740-749.(CHEN Bingrui，FENG Xiating，LI Shulin，et al. Microseismic source layering location method of rock masses based on particle swarm optimization algorithm[J]. Chinese Journal of Rock Mechanics and Engineering，2009，28(4)：740-749.(in Chinese))
[3]	李庶林，尹贤刚，郑文达，等. 凡口铅锌矿多通道微震监测系统及其应用研究[J]. 岩石力学与工程学报，2005，24(12)：2 048-2 053. (LI Shulin，YIN Xiangang，ZHENG Wenda，et al. Multichannel microseismic monitoring system and its application in Fankou lead-zinc mine[J]. Chinese Journal of Rock Mechanics and Engineering，2005，24(12)：2 048-2 053.(in Chinese))
[13]	XU N W，TANG C A，LI L C，et al. Microseismic monitoring and stability analysis of the left bank slope in Jinping first stage hydropower station in Southwestern China[J]. International Journal of Rock Mechanics and Mining Sciences，2011，48(6)：950-963.
[23]	WOO S，PARK J，LEE J Y，et al. CBAM：convolutional block attention module[C]// European Conference on Computer Vision. [S. l.]：[s. n.]，2018：3-19.
[33]	LI Y J，SUI Q M，WANG J，et al. Localization of microseismic source based on genetic-simplex hybrid algorithm[C]// IEEE Chinese Automation Congress. [S. l]：[s. n.]，2017：4 002-4 007.
[10]	FENG X T，YU Y，FENG G L，et al. Fractal behaviour of the microseismic energy associated with immediate rockbursts in deep，hard rock tunnels[J]. Tunnelling and Underground Space Technology，2016，51：98-107.
[20]	AKHTAR S，CHAUHAN S，EKBAL A. A deep multi-task contextual attention framework for multi-modal affect analysis[J]. ACM Transactions on Knowledge Discovery from Data，2020，14(3)：1-27.
[30]	赵明，陈石，房立华，等. 基于U形卷积神经网络的震相识别与到时拾取方法研究[J]. 地球物理学报，2019，62(8)：3 034- 3 042.(ZHAO Ming，CHEN Shi，FANG Lihua，et al. Study on phase recognition and picking method based on U-shaped convolutional neural network[J]. Chinese Journal of Geophysics，2019，62(8)： 3 034-3 042.(in Chinese))
[18]	BALTRUAITIS T，AHUJA C，MORENCY L. Multimodal machine learning：a survey and taxonomy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2019，41(2)：423-443.
[26]	HE K M，ZHANG X Y，REN S Q，et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). [S. l.]：[s. n.]，2016：770-778.
[28]	ALLEN V. Automatic earthquake recognition and timing from single traces[J]. Bulletin of the Seismological Society of America，1978，68(5)：1 521-1 532.
[38]	MA K，SHEN Q Q，SUN X Y，et al. Rockburst prediction model using machine learning based on microseismic parameters of Qinling water conveyance tunnel[J]. Journal of Central South University，2023，30(1)：289-305.
[36]	王剑锋，李天斌，马春驰，等. 基于引力搜索法的隧道围岩微震定位研究[J]. 岩土力学，2019，40(11)：4 421-4 428.(WANG Jianfeng，LI Tianbin，MA Chunchi，et al. Research on micro-seismic positioning of tunnel surrounding rock based on gravity search algorithm[J]. Rock and Soil Mechanics，2019，40(11)：4 421-4 428.(in Chinese))