Intelligent Microseismic Monitoring and Early Warning for Rock Burst During TBM Excavation of Deep Metal Mines

doi:10.12068/j.issn.1005-3026.2025.20250085

Abstract

Abstract:

In response to the problem of insufficient automation and intelligence in the microseismic monitoring and early warning for rock bursts during tunnelling boring machine （TBM） excavation of deep metal mines， research on multi-dimensional parameter recognition of drilling holes based on deep machine vision DPED-AT method was conducted； automatic disassembly and assembly device for microseismic sensors was developed， and the decision-making system was designed， achieving automatic disassembly and assembly of microseismic sensors during TBM excavation. Microseismic intelligent frequency conversion acquisition technology was developed， realizing continuous and high-fidelity acquisition of rock rupture information during the rock burst incubation process. An improved neural network algorithm for identifying and picking up rupture signals was proposed， as well as a three-dimensional characterization algorithm for the probability field of microseismic sources induced by rock bursts incubation. Intelligent interpretation and refined early warning of rock burst incubation information during TBM excavation were preliminarily realized， and an intelligent monitoring and early warning technology system for rock burst that integrated intelligent drilling hole recognition， automatic sensor disassembly and assembly， and intelligent signal acquisition and interpretation was ultimately established. The application in Ruihai Gold Mine shows that it has achieved automatic microseismic monitoring， interpretation， and early warning of rock burst， providing strong support for less manned and unmanned TBM excavation in deep metal mines.

Key words: tunnelling boring machine, microseismic monitoring, early warning of rock burst, automatic disassembly and assembly, intelligent acquisition, intelligent recognition

CLC Number:

TD 326

Bing-rui CHEN, Xu WANG, Gui-peng JIANG, Fei HE, Jia-lin HAN, Jian-jun HAO. Intelligent Microseismic Monitoring and Early Warning for Rock Burst During TBM Excavation of Deep Metal Mines[J]. Journal of Northeastern University(Natural Science), 2025, 46(7): 148-162.

Figures/Tables 20

Fig.1 Flow chart of intelligent microseismic monitoring and early warning for rock burst

Fig.2 Detection effect of drilling hole contour under different drilling conditions

Fig.3 Automatic disassembly and assembly system for microseismic sensors during TBM excavation

Fig.4 Flowchart for intelligent disassembly and assembly of microseismic sensors during TBM excavation

Fig.5 Flow chart of intelligent acquisition technology

Fig.6 Waveform comparison with different sampling frequencies

Fig.7 ECNN model structure [50]

Table 1 Recognition results of samples with different

方法	准确率/%
方法	0 dB	5 dB	10 dB	15 dB	20 dB	25 dB
CNN	88.5	89.5	92.0	94.4	94.9	94.9
ECNN	94.7	94.5	96.0	96.0	96.0	96.3

Fig.8 Flow chart of real-time pickup method of microseismic signal arrival based on CRNN

Fig.9 CRNN model structure

Fig.10 Variation of average pickup errors of each method with SNR

Fig.11 Schematic diagram of microseismic source location based on uncertainty

Fig.12 Schematic diagram of rock burst probability field

Fig.13 Distribution of risk zones corresponding to different rock burst probabilities

Fig.14 Intelligent disassembly and assembly of microseismic sensors

Fig.15 Waveform of rock rupture signal before frequency conversion (sampling frequency of 1 000 Hz)

Fig.16 Waveform of rock rupture signal after frequency conversion (sampling frequency of 4 000 Hz)

Fig.17 Recognition results of TBM microseismic signal by ECNN algorithm

Fig.18 Comparison of P and S wave pickup accuracy of each method

Fig.19 Spatial distribution of rock burst risk zones

References 63

[1]	陈炳瑞，冯夏庭，曾雄辉，等. 深埋隧洞TBM掘进微震实时监测与特征分析［J］. 岩石力学与工程学报， 2011， 30（2）： 275-283.
	Chen Bing-rui， Feng Xia-ting， Zeng Xiong-hui， et al. Real-time microseismic monitoring and its characteristic analysis during tbm tunneling in deep-buried tunnel［J］. Chinese Journal of Rock Mechanics and Engineering， 2011， 30（2）： 275-283.
[2]	Li P X， Chen B R， Xiao Y X， et al. Rockburst and microseismic activity in a lagging tunnel as the spacing between twin TBM excavated tunnels changes： a case from the Neelum-Jhelum hydropower project［J］. Tunnelling and Underground Space Technology， 2023， 132： 104884.
[3]	冯欢欢，洪开荣，杨延栋，等. 极端复杂地质条件下TBM隧道施工关键技术研究及应用［J］. 现代隧道技术， 2022， 59（1）： 42-54.
	Feng Huan-huan， Hong Kai-rong， Yang Yan-dong， et al. Research and application of key construction technologies for TBM-driven tunnels under extreme complex geological conditions［J］. Modern Tunnelling Technology， 2022， 59（1）： 42-54.
[4]	刘泉声，刘滨，唐彬，等. 煤矿深部巷道碎胀大变形灾害控制及大变形灾变环境下TBM快速成巷技术［J］. 煤炭学报， 2025， 50（1）： 224-244.
	Liu Quan-sheng， Liu Bin， Tang Bin， et al. The control of fragmenting swelling deformation disasters in deep mine roadways and the efficient TBM tunneling technology under such conditions［J］. Journal of China Coal Society， 2025， 50（1）： 224-244.
[5]	刘泉声，黄兴，潘玉丛，等. TBM在煤矿巷道掘进中的技术应用和研究进展［J］. 煤炭科学技术， 2023， 51（1）： 242-259.
	Liu Quan-sheng， Huang Xing， Pan Yu-cong， et al. Application and research progress of TBM tunneling in coal mine roadway［J］. Coal Science and Technology， 2023， 51（1）： 242-259.
[6]	钱七虎. 地下工程建设安全面临的挑战与对策［J］. 岩石力学与工程学报， 2012， 31（10）： 1945-1956.
	Qian Qi-hu. Challenges faced by underground projects construction safety and countermeasures［J］. Chinese Journal of Rock Mechanics and Engineering， 2012， 31（10）： 1945-1956.
[7]	Ma T H， Tang C N， Tang S B， et al. Rockburst mechanism and prediction based on microseismic monitoring［J］. International Journal of Rock Mechanics and Mining Sciences， 2018， 110： 177-188.
[8]	Feng G L， Chen B R， Xiao Y X， et al. Microseismic characteristics of rockburst development in deep TBM tunnels with alternating soft-hard strata and application to rockburst warning： a case study of the Neelum-Jhelum hydropower project［J］. Tunnelling and Underground Space Technology， 2022， 122： 104398.
[9]	Cao A Y， Dou L M， Wang C B， et al. Microseismic precursory characteristics of rock burst hazard in mining areas near a large residual coal pillar： a case study from Xuzhuang Coal Mine， Xuzhou， China［J］. Rock Mechanics and Rock Engineering， 2016， 49（11）： 4407-4422.
[10]	Feng G L， Feng X T， Chen B R， et al. Microseismic sequences associated with rockbursts in the tunnels of the Jinping II hydropower station［J］. International Journal of Rock Mechanics and Mining Sciences， 2015， 80： 89-100.
[11]	陈炳瑞，王旭，朱新豪，等.一种TBM搭载微震传感器自动拆装装置： 202010136759.8［P］.2021-03-23.
	Chen Bing-rui， Wang Xu， Zhu Xin-hao， et al. A TBM equipped with an automatic installation and removal device for microseismic sensors： 202010136759.8［P］.2021-03-23.
[12]	张鹏海，程关文，杨天鸿，等. 在破碎易塌孔岩体中安装和回收微震传感器的装置及方法： 201910033928.2［P］.2025-05-18.
	Zhang Peng-hai， Cheng Guan-wen， Yang Tian-hong， et al. Device and method for installing and recovering microseismic sensor in broken collapsible rock mass： 201910033928.2［P］.2025-05-18.
[13]	丰光亮，冯夏庭，陈炳瑞，等. 一种孔内及孔外双重固定可回收式微震传感器安装方法： 201711406469.5［P］. 2020-07-10.
	Feng Guang-liang， Feng Xia-ting， Chen Bing-rui， et al. Hole inside and hole outside double-fixing recyclable micro-seismic sensor installation method： 201711406469.5［P］. 2020-07-10.
[14]	陈东方，游喻豪，任高峰，等. 一种基于热熔胶的微震传感器孔内安装回收装置： 201910299197.6 ［P］. 2021-05-18.
	Chen Dong-fang， You Yu-hao， Ren Gao-feng， et al. In-hole installation and recovery device for micro-seismic sensor on the basis of hot melt adhesive： 201910299197.6 ［P］. 2021-05-18.
[15]	Nippress S E J， Rietbrock A， Heath A E. Optimized automatic pickers： application to the ANCORP data set［J］. Geophysical Journal International， 2010， 181（2）： 911-925.
[16]	Saragiotis C D， Hadjileontiadis L J， Panas S M. PAI-S/K： a robust automatic seismic P phase arrival identification scheme［J］. IEEE Transactions on Geoscience and Remote Sensing， 2002， 40（6）： 1395-1404.
[17]	Allen R V. Automatic earthquake recognition and timing from single traces［J］. Bulletin of the Seismological Society of America， 1978， 68（5）： 1521-1532.
[18]	Allen R. Automatic phase pickers： their present use and future prospects［J］. Bulletin of the Seismological Society of America， 1982， 72（6B）： S225-S242.
[19]	Li X B， Shang X Y， Morales-Esteban A， et al. Identifying P phase arrival of weak events： the akaike information criterion picking application based on the empirical mode decomposition［J］. Computers & Geosciences， 2017， 100： 57-66.
[20]	Maeda N. A method for reading and checking phase time in auto-processing system of seismic wave data［J］. Zisin， 1985， 38（3）： 365-379.
[21]	Zhu D， Li Y， Zhang C. Automatic time picking for microseismic data based on a fuzzy C-means clustering algorithm［J］. IEEE Geoscience and Remote Sensing Letters， 2016， 13（12）： 1900-1904.
[22]	Ma H T， Wang T， Li Y， et al. A time picking method for microseismic data based on LLE and improved PSO clustering algorithm［J］. IEEE Geoscience and Remote Sensing Letters， 2018， 15（11）： 1677-1681.
[23]	Zhu X H， Chen B R， Wang X， et al. Time series segmentation clustering： a new method for S-phase picking in microseismic data［J］. IEEE Geoscience and Remote Sensing Letters， 2021， 19： 7504605.
[24]	Gibowicz S J， Kijko A. An introduction to mining seismology［M］. London： Academic Press Limited， 1994.
[25]	Kijko A， Sciocatti M. Optimal spatial distribution of seismic stations in mines［J］. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts， 1995， 32（6）： 607-615.
[26]	曹安业，王常彬，杨旭，等. 微震定位精度影响下采场裂隙表征与冲击地压预警［J］. 煤炭科学技术， 2024， 52（2）： 1-9.
	Cao An-ye， Wang Chang-bin， Yang Xu， et al. Fractures characterization in mining field considering seismic location accuracy and its application on pre-warning coal burst hazards［J］. Coal Science and Technology， 2024， 52（2）： 1-9.
[27]	国家能源局. 水电工程岩体稳定性微震监测技术规范：［S］. 北京：中国电力出版社， 2021.
	National Energy Bureau of the People’s Republic of China. Technical code for microseismic monitoring of rockmass stability of hydropower projects：［S］. Beijing： China Electric Power Press， 2021.
[28]	王旭.TBM施工岩爆微震全自动监测与预警信息三维表征研究［D］.武汉：中国科学院大学，2024.
	Wang Xu. Research on three-dimensional representation of microseismic automatic monitoring and early warning information for rockburst during construction［D］.Wuhan： University of Chinese Academy of Sciences，2024.
[29]	陈炳瑞，王旭. 一种TBM施工岩爆微震自动监测智能预警系统及方法： 202210315604.X ［P］. 2022-06-24.
	Chen Bing-rui， Wang Xu. TBM construction rockburst micro-seismic automatic monitoring and intelligent early warning system and method： 202210315604.X［P］. 2022-06-24.
[30]	王旭，陈炳瑞，冯夏庭，等. 一种TBM搭载微震传感器自动部署装置： 202011004514.6 ［P］. 2021-01-05.
	Wang Xu， Chen Bing-rui， Feng Xia-ting， et al. TBM-carried micro-seismic sensor automatic deployment device： 202011004514.6 ［P］. 2021-01-05.
[31]	陈炳瑞，郝剑钧，王旭，等. 一种TBM施工轨缝自适应跨越行走装置和方法： 202211272850.8［P］. 2023-02-03.
	Chen Bing-rui， Hao Jian-jun， Wang Xu， et al. TBM construction rail seam self-adaptive spanning walking device and TBM construction rail seam self-adaptive spanning walking method： 202211272850.8 ［P］. 2023-02-03.
[32]	王旭，巩晓雯，黄其帅，等. 基于深度机器视觉的香炉山隧洞钻孔多维特征精准定位［J］. 清华大学学报（自然科学版）， 2024， 64（7）： 1278-1292.
	Wang Xu， Gong Xiao-wen， Huang Qi-shuai， et al. Precise multi-dimensional features positioning of Xianglushan tunnel drilling based on deep-machine vision［J］. Journal of Tsinghua University （Science and Technology）， 2024， 64（7）： 1278-1292.
[33]	王旭，陈炳瑞，谢厚霖，等. 一种基于机器视觉的钻孔多维特征精准识别方法： 202311441411.X ［P］. 2024-02-06.
	Wang Xu， Chen Bing-rui， Xie Hou-lin， et al. Drilling multi-dimensional feature accurate recognition method based on machine vision： 202311441411.X［P］. 2024-02-06.
[34]	王旭，陈炳瑞，肖亚勋，等. 一种复杂环境钻孔轮廓精准拟合方法： 202410652956.3［P］. 2024-08-30.
	Wang Xu， Chen Bing-rui， Xiao Ya-xun， et al. Precise fitting method for drilling contour in complex environment： 202410652956.3 ［P］. 2024-08-30.
[35]	Wang W， Wang G， Hu C L， et al. Robust ellipse fitting based on maximum correntropy criterion with variable center［J］. IEEE Transactions on Image Processing， 2023， 32： 2520-2535.
[36]	Fornaciari M， Prati A， Cucchiara R. A fast and effective ellipse detector for embedded vision applications［J］. Pattern Recognition， 2014， 47（11）： 3693-3708.
[37]	贾棋，梁景朝，王祎，等. 基于区域检测和弧筛选的椭圆检测方法［J］. 计算机辅助设计与图形学学报， 2022， 34（11）： 1784-1794.
	Jia Qi， Liang Jing-chao， Wang Yi， et al. Ellipse detection combining region detection and arc filtering［J］. Journal of Computer-Aided Design & Computer Graphics， 2022， 34（11）： 1784-1794.
[38]	Otsu N. A threshold selection method from gray-level histograms［J］. IEEE Transactions on Systems， Man， and Cybernetics， 1979， 9（1）： 62-66.
[39]	魏振忠，张广军. 视觉检测中椭圆中心成像畸变误差模型研究［J］. 北京航空航天大学学报， 2003， 29（2）： 140-143.
	Wei Zhen-zhong， Zhang Guang-jun. Distortion error model of image of ellipse center in 3D visual inspection［J］. Journal of Beijing University of Aeronautics and Astronautics， 2003， 29（2）： 140-143.
[40]	赵志成，罗泽，王鹏彦，等. 基于深度残差网络图像分类算法研究综述［J］. 计算机系统应用， 2020， 29（1）： 14-21.
	Zhao Zhi-cheng， Luo Ze， Wang Peng-yan， et al. Survey on image classification algorithms based on deep residual network［J］. Computer Systems & Applications， 2020， 29（1）： 14-21.
[41]	Miller J R， Goldman R N. Using tangent balls to find plane sections of natural quadrics［J］. IEEE Computer Graphics and Applications， 1992， 12（2）： 68-82.
[42]	蒋弘毅，王永娟，康锦煜. 目标检测模型及其优化方法综述［J］. 自动化学报， 2021， 47（6）： 1232-1255.
	Jiang Hong-yi， Wang Yong-juan， Kang Jin-yu. A survey of object detection models and its optimization methods［J］. Acta Automatica Sinica， 2021， 47（6）： 1232-1255.
[43]	Jia Q， Fan X， Luo Z X， et al. A fast ellipse detector using projective invariant pruning［J］. IEEE Transactions on Image Processing， 2017， 26（8）： 3665-3679.
[44]	Lu C S， Xia S Y， Shao M， et al. Arc-support line segments revisited： an efficient high-quality ellipse detection［J］. IEEE Transactions on Image Processing， 2019， 29： 768-781.
[45]	Meng C， Li Z X， Bai X Z， et al. Arc adjacency matrix-based fast ellipse detection［J］. IEEE Transactions on Image Processing， 2020， 29： 4406-4420.
[46]	Varghese R M S. YOLOv8： a novel object detection algorithm with enhanced performance and robustness［C］//2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems （ADICS）. Chennai， 2024： 1-6.
[47]	陈炳瑞，王旭，朱新豪. 一种嵌入式TBM搭载微震监测装置： 201911368376.7 ［P］. 2021-01-29.
	Chen Bing-rui， Wang Xu， Zhu Xin-hao. Embedded TBM carried micro-seismic monitoring device： 201911368376.7 ［P］. 2021-01-29.
[48]	陈炳瑞，李华方，杨为，等. 一种高精度智能变频微震采集方法： 202410856722.0 ［P］. 2024-10-01.
	Chen Bing-rui， Li Hua-fang， Yang Wei， et al. High-precision intelligent variable-frequency micro-seismic acquisition method： 202410856722.0 ［P］. 2024-10-01.
[49]	Nyquist H. Certain factors affecting telegraph speed［J］. Transactions of the American Institute of Electrical Engineers， 1924， 3（2）： 324-346.
[50]	Wang X， Chen B R， Ran Y C， et al. Application of deep learning in microseismic waveform classification： a case study of the Yebatan hydropower station project［C］//2022 8th International Conference on Hydraulic and Civil Engineering： Deep Space Intelligent Development and Utilization Forum （ICHCE）.Xi’an， 2022：1292-1298.
[51]	Chen B R， Wang X， Zhu X H， et al. Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application［J］. Journal of Rock Mechanics and Geotechnical Engineering， 2024， 16（3）： 761-777.
[52]	Zhang J L， Sheng G Q. First arrival picking of microseismic signals based on nested U-Net and Wasserstein generative adversarial network［J］. Journal of Petroleum Science and Engineering， 2020， 195： 107527.
[53]	丰光亮. 基于微震信息的深埋隧洞岩爆孕育过程的动态预警方法研究［D］. 北京：中国科学院大学， 2015.
	Feng Guang-liang. Study on dynamic early warning method of rock burst gestation process in deep-buried tunnel based on microseismic information［D］. Beijing： University of Chinese Academy of Sciences， 2015.
[54]	杜岩，谢谟文，蒋宇静，等.基于固有振动频率的危岩安全监测试验研究［J］. 岩土力学， 2016， 37（10）：3035-3040.
	Du Yan， Xie Mo-wen， Jiang Yu-jing， et al. Safety monitoring experiment of unstable rock based on natural vibration frequency［J］. Rock and Soil Mechanics， 2016， 37（10）： 3035-3040.
[55]	姜彤，万里，黄坤，等. 基于固有频率的岩石压剪断裂演化过程试验研究［J］. 中国矿业大学学报， 2024， 53（5）： 901-914.
	Jiang Tong， Wan Li， Huang Kun， et al. Experimental investigation on the evolution of rock compression-shear fracture using the natural frequency［J］. Journal of China University of Mining & Technology， 2024， 53（5）： 901-914.
[56]	蒋宇静，吴法博，张学朋，等. 基于激光多普勒测振的混凝土损伤量化评价研究［J］. 中国矿业大学学报， 2023， 52（5）： 889-903.
	Jiang Yu-jing， Wu Fa-bo， Zhang Xue-peng， et al. Non-destructive quantitative assessment on the damage of concrete structure based on laser Doppler vibrometer technology［J］. Journal of China University of Mining & Technology， 2023， 52（5）： 889-903.
[57]	陈晨，谢谟文，孙广存，等. 基于多时域指标的危岩体崩塌安全评价模型研究［J］. 矿业研究与开发， 2022， 42（4）： 45-49.
	Chen Chen， Xie Mo-wen， Sun Guang-cun， et al. Research on safety evaluation model of dangerous rock mass collapse based on multiple time domain indicators［J］. Mining Research and Development， 2022， 42（4）： 45-49.
[58]	贺宁波，范庆龙，侯争军，等. 非接触式应急监测技术在震区高位危岩崩塌边坡中的应用研究［J］. 水电站设计， 2024， 40（2）： 93-96， 103.
	He Ning-bo， Fan Qing-long， Hou Zheng-jun， et al. Non-contact emergency monitoring technology for high level slope dangerous rockfall in earthquake region［J］. Design of Hydroelectric Power Station， 2024， 40（2）： 93-96， 103.
[59]	韩学辉，王桂香. 氡法监测地震的理想曲线模型探讨［J］. 太原理工大学学报， 2000， 31（1）： 101-103.
	Han Xue-hui， Wang Gui-xiang. Theideal curve model on earthquake prediction by radon monitoring［J］. Journal of Taiyuan University of Technology， 2000， 31（1）： 101-103.
[60]	范树全，高清武. 氡预报地震的实验研究［J］. 地震地质， 1982， 4（1）： 45-56.
	Fan Shu-quan， Gao Qing-wu. Experimental study for earthquake prediction by radon anomalies［J］. Seismology and Geology， 1982， 4（1）： 45-56.
[61]	Mollo S， Tuccimei P， Heap M J， et al. Increase in radon emission due to rock failure： an experimental study［J］. Geophysical Research Letters， 2011， 38（14）： L14304.
[62]	Girault F， Schubnel A， Pili É. Transient radon signals driven by fluid pressure pulse， micro-crack closure， and failure during granite deformation experiments［J］. Earth and Planetary Science Letters， 2017， 474： 409-418.
[63]	Li L Y， Zhang C Q， Tao Z G， et al. Study on radon release of intact rocks during direct shear［J］. Journal of Central South University， 2022， 29（12）： 4021-4034.