Real-time recognition algorithm for microseismic signals of rock failure based on STA/LTA and its engineering application

CHEN Bing-rui; WU Hao; CHI Xiu-wen; LIU Hui; WU Meng-die; YAN Jun-wei

doi:10.16285/j.rsm.2018.1017

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Real-time recognition algorithm for microseismic signals of rock failure based on STA/LTA and its engineering application

更新时间：2026-06-25

- Real-time recognition algorithm for microseismic signals of rock failure based on STA/LTA and its engineering application
- Vol. 40, Issue 9, Pages: 3689-3696(2019)
- 作者机构：
  
  1. 中国科学院武汉岩土力学研究所岩土力学与工程国家重点实验室,湖北,武汉,430071
  2. 北方爆破科技有限公司,北京,100089
  3. 武汉理工大学资源与环境工程学院,湖北,武汉,430070
  4. 成都理工大学环境与土木工程学院,四川,成都,610059
  5. 湖北工业大学土木建筑与环境学院,湖北,武汉,430068
  6. 沈阳军区司令部工程科研设计院,辽宁,沈阳,110162
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2018.1017
  CLC： TU457
- Online First：08 September 2019，
  
  Published：2019
- 稿件说明：
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CHEN Bing-rui, WU Hao, CHI Xiu-wen, et al. Real-time recognition algorithm for microseismic signals of rock failure based on STA/LTA and its engineering application[J]. 2019, 40(9): 3689-3696.
DOI：

CHEN Bing-rui, WU Hao, CHI Xiu-wen, et al. Real-time recognition algorithm for microseismic signals of rock failure based on STA/LTA and its engineering application[J]. 2019, 40(9): 3689-3696. DOI： 10.16285/j.rsm.2018.1017.

摘要

微震监测获取的数据中通常混有大量的非岩石破裂信号，该类信号目前主要通过人工经验进行识别与滤除，这消耗了大量的宝贵时间，严重影响灾害的防治和救援效率。对大量微震信号进行分析，发现STA/LTA算法在信号实时触发后能大致表征波形振幅和频率的变化，岩石破裂信号和非岩石破裂信号在延迟位置处R值具有差异性。基于此，提出了岩石破裂微震信号实时识别算法。新算法应用到白鹤滩水电站地下厂房、红透山和阿舍勒铜矿深部采场3个工程，岩石破裂事件识别的准确率分别是85.98%、92.45%和91.06%，非岩石破裂事件滤除的准确率分别是72.06%、83.11%和49.87%。该算法使基于岩石破裂微震信息的岩石工程灾害自动分析与预警成为可能，具有重要意义。

Abstract

A large number of non-rock failure signals are usually obtained by microseismic monitoring. At present

these kinds of signals are mainly identified and filtered by manual experience

which consumes much precious time and seriously affects the efficiency of disaster prevention and rescue. By analysing massive microseismic signals

we find that the STA/LTA algorithm can roughly characterise the amplitude and frequency of waveforms after the real-time triggering of signals. The R values of the rock failure signals and the non-rock failure signals are different in the delayed position. Therefore

a real-time recognition algorithm for the microseismic signal of rock failure is proposed. The new algorithm is applied to three projects

including the underground powerhouse of the Baihetan hydropower station

the deep stope of the Hongtoushan copper mine

and the deep stope of the Ashele copper mine. The accuracy rates of the identification of rock failure events are 85.98%

92.45% and 91.06%

respectively. The accuracy rates of the filtering of non-rock failure events are 72.06%

83.11% and 49.87%

respectively. It is of great significance that the algorithm makes it possible to automatically analyse and predict rock engineering disasters based on microseismic information of rock failure.

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