Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices
Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices
复制标题
利用微震多参数指标进行岩爆综合预警
DOI：
10.1016/j.ijmst.2018.08.007
复制
发表时间：
2018-09-01
影响因子：
11.8
通讯作者：
Guo, Wenhao
中科院分区：
文献类型：
--
作者：
Dou, Linming;Cai, Wu;Guo, Wenhao
冲击地压已成为煤矿井下开采最严重的危险之一，其预警是安全管理的重要组成部分。微地震监测被认为是一种潜在的预测冲击地压的有力工具。在归一化煤岩动力破坏多信息预警模型的基础上，建立了煤岩动力破坏的MS多参数指标体系，估算了各指标的临界值。该指标体系包括破裂应变能(BSE)指标、时空震级独立信息(TSMII)指标和时空震级复合信息(TSMCI)指标。在此多参数指标体系的基础上，引入R值评分法计算各指标的权重，进行了综合分析。为了标定多参数指标体系和相关的综合分析，首先利用胡家河煤矿(中国)LW 402102历时4个月的MS历史数据确定了各指标的权重。然后将该校正后的MS多参数综合分析系统应用于LW 402103后续冲击地压事件的预警。结果表明，该多参数指标体系与综合分析相结合，能够对冲击地压风险进行定量预警。(C)爱思唯尔集团代表中国矿业大学出版的《2018》。
Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic (MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy (BSE) index, time-space-magnitude independent information (TSMII) indices and time-space-magnitude compound information (TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW 402102 of Hujiahe Coal Mine (China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk. (C) 2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology.