Experimental study of rockburst early warning method based on acoustic emission cluster analysis and neural network identification

ZHANG Yan-bo; YANG Zhen; YAO Xu-long; LIANG Peng; TIAN Bao-zhu; SUN Lin

doi:10.16285/j.rsm.2017.S2.012

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Experimental study of rockburst early warning method based on acoustic emission cluster analysis and neural network identification

更新时间：2026-06-25

- Experimental study of rockburst early warning method based on acoustic emission cluster analysis and neural network identification
- Vol. 38, Issue S2, Pages: 89-98(2017)
- 作者机构：
  
  1. 华北理工大学矿业工程学院,河北,唐山,063009
  2. 华北理工大学河北省矿业开发与安全技术重点实验室,河北,唐山,063009
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2017.S2.012
  CLC： TU452
- Online First：05 June 2018，
  
  Published：2017
- 稿件说明：
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ZHANG Yan-bo, YANG Zhen, YAO Xu-long, et al. Experimental study of rockburst early warning method based on acoustic emission cluster analysis and neural network identification[J]. 2017, 38(S2): 89-98.
DOI：

ZHANG Yan-bo, YANG Zhen, YAO Xu-long, et al. Experimental study of rockburst early warning method based on acoustic emission cluster analysis and neural network identification[J]. 2017, 38(S2): 89-98. DOI： 10.16285/j.rsm.2017.S2.012.

摘要

开展巷道岩爆室内模拟声发射监测实验，对岩爆过程中的声发射信号进行聚类分析，得到3种类型声发射信号，优选具有岩爆前兆演化异常、能量大、数量小等特征的信号类型作为岩爆前兆特征信号。岩爆发生前，岩爆前兆特征信号开始密集出现，明显的前兆异常。运用时间窗函数对岩爆前兆特征信号的时间密集性进行量化分析，提出衡量信号时序演化特征的量化指标即时间密集度。岩爆发生前，前兆信号密集出现，出现了 ≥3值，可将首次 ≥3作为岩爆前兆信息，可将岩爆预警阈值a划定为3。基于岩爆前兆特征信号优选和预警阈值提取，借助BP神经网络，智能识取岩爆前兆特征信号，利用时间窗函数计算岩爆前兆特征信号值，当岩爆前兆特征信号值开始达到岩爆预警阈值（ ≥a）时则进行岩爆灾害预警，建立了基于巷道岩爆模拟实验声发射数据的岩爆实时预警方法，为工程现场岩爆预警方法的建立提供了新思路。

Abstract

Three types of AE signals are obtained by carrying out the tunnel rock burst simulation experiment and clustering rock explosion signal. The acoustic emission signals which have the characteristics of the precursory anomaly

large energy and small quantity

are preferred as precursor characteristic signals of rockburst. Before the rockburst

the precursor characteristic signal of rockburst appeared densely; it is shown obvious precursory anomaly. The window functionis used to carry out the quantitative analysis of temporal density of precursor signal and put forward the quantitative index——time intensity which can measure timing evolution characteristics of signal. Before the rockburst

the precursory signal appeared densely

is greater than or equal to 3. This situation firstly appears can be as the precursor information of rockburst; therefore the early warning threshold a can be set to 3. Based on optimization of rockburst precursor signal and early warning threshold extraction

with the help of BP neural network

intelligently identify rockburst precursor signal. The time window function is used to calculate value of precursor signal. When value of precursor signal start to achieve rockburst warning threshold ( ≥a)

it starts to warn rockburst. Therefore

a real-time warning method of rockburst based on AE data of tunnel rockburst simulation experiment is established so as to provides a new idea for the construction of early warning method of rockburst.

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