深部金属矿TBM开拓岩爆微震智能监测与预警

doi:10.12068/j.issn.1005-3026.2025.20250085

摘要/Abstract

摘要：

针对深部金属矿隧道掘进机(tunnelling boring machine, TBM)开拓岩爆微震监测与预警自动化、智能化不足的问题，开展了基于深度机器视觉DPED（drilling profile ellipse detection）-AT(accurate location of drilling multidimensional features based on anchor tracking)方法的钻孔多维参数识别研究、微震传感器自动拆装装置研发与决策系统设计，实现了TBM开拓微震传感器自动拆装；研发了微震智能变频采集技术，实现了岩爆孕育过程岩石破裂信息连续、保真采集；研发了改进神经网络破裂信号识别与到时实时拾取算法，及岩爆孕育微震源概率场三维表征算法，初步实现TBM开拓岩爆孕育信息智能解译与精细化预警，最终建立了融合钻孔智能识别、传感器自动拆装、信号智能采集-解译的岩爆智能监测预警技术体系.招金矿业瑞海金矿应用表明，该技术初步实现了岩爆微震自动监测、解译与预警，为深部金属矿TBM开拓的少人化、无人化提供有力支撑.

关键词: 隧道掘进机, 微震监测, 岩爆预警, 自动拆装, 智能采集, 智能识别

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

中图分类号:

TD 326

陈炳瑞, 王旭, 姜桂鹏, 贺飞, 韩佳霖, 郝剑钧. 深部金属矿TBM开拓岩爆微震智能监测与预警[J]. 东北大学学报（自然科学版）, 2025, 46(7): 148-162.

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.

图/表 20

图1 岩爆微震智能监测与预警流程图

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

图2 不同钻孔条件下的钻孔轮廓检测效果（a）—孔底有碎石的浅钻孔；（b）—倾斜钻孔；（c）—有阴影的钻孔；（d）—有阴影且孔口凸凹不平的钻孔.

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

图3 TBM开拓微震传感器自动拆装系统

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

图4 TBM开拓微震传感器智能拆装流程图

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

图5 智能采集技术流程图

Fig.5 Flow chart of intelligent acquisition technology

图6 不同采样频率采集波形对比（a）—智能变频；（b）—500 Hz固定频率；（c）—8 000 Hz固定频率.

Fig.6 Waveform comparison with different sampling frequencies

图7 ECNN模型结构[50]

Fig.7 ECNN model structure [50]

表1 不同信噪比样本的识别结果 (signal-to-noise ratios)

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

图8 基于CRNN的微震信号到时实时拾取方法流程图

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

图9 CRNN模型结构

Fig.9 CRNN model structure

图10 各方法平均拾取误差随信噪比变化（a）—P波；（b）—S波.

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

图11 基于不确定性的微震源位置示意图

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

图12 岩爆概率场示意图

Fig.12 Schematic diagram of rock burst probability field

图13 不同岩爆概率对应的风险区域分布

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

图14 微震传感器智能拆装

Fig.14 Intelligent disassembly and assembly of microseismic sensors

图15 变频前岩石破裂信号波形图（1 000 Hz采样频率）

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

图16 变频后岩石破裂信号波形图（4 000 Hz采样频率）

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

图17 TBM微震信号ECNN算法识别结果

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

图18 各方法的 P、S 波拾取精度对比

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

图19 岩爆风险区域空间分布

Fig.19 Spatial distribution of rock burst risk zones

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