冲击危险区域多元地球物理指标动态辨识方法及其应用  

Dynamic identification method for rockburst hazard areas based on multivariate geophysical indicators and its application

作　　者：王爱文 李超 潘一山 宋义敏[3] 范德威 魏传奇 于新河 卢闯 WANG Aiwen;LI Chao;PAN Yishan;SONG Yimin;FAN Dewei;WEI Chuanqi;YU Xinhe;LU Chuang(College of Mining Engineering,Liaoning Technical University,Fuxin 123000,China;Institute of Disaster Rock Mechanics,Liaoning University,Shenyang 110036,China;School of Civil Engineering,North China University of Technology,Beijing 100144,China;Jilin Longjiapu Mining Co.,Ltd.,Changchun 130504,China;Environmental Engineering College,Liaoning University,Shenyang 110036,China)

机构地区：[1]辽宁工程技术大学矿业学院,辽宁阜新123000 [2]辽宁大学灾害岩体力学研究所,辽宁沈阳110036 [3]北方工业大学土木工程学院,北京100144 [4]吉林省龙家堡矿业有限责任公司,吉林长春130504 [5]辽宁大学环境学院,辽宁沈阳110036

出　　处：《煤炭学报》2024年第6期2573-2588,共16页Journal of China Coal Society

基　　金：国家重点研发计划资助项目(2022YFC3004605);国家自然科学基金面上资助项目(52374201,51974150)。

摘　　要：精准识别冲击危险区域并给出危险程度及其演化规律对冲击地压防治具有重要意义。采用变形局部化与多元地球物理指标空间扫描相结合的方法,探究大能量事件发生区域的微震前兆特征,追踪冲击危险区域的动态演化过程。基于变形局部化原理,利用梯度显著性指标识别变形局部化区域,圈定冲击危险区域;采用滑动窗扫描方法,研究了变形局部化区域内的b、A(b)、S等物理指标空间分布特征,以掘进期间梯度显著性指标识别的微震聚集区域内大能量微震事件对应的b、A(b)、S、△F、A(t)作为划分工作面回采期间冲击危险等级的阈值;利用贝叶斯网络法分析各个物理指标预测危险区域的效能,构建综合预测危险区域模型,计算物理指标权重并得到综合预测指标,并以513工作面进行实例分析。结果表明:地球物理指标可以识别微震聚集信号,判断危险区域,根据513工作面实际监测数据判断出3个微震事件聚集区域;物理指标的空间扫描结果与微震数据的聚集区域具有同步的特征,大能量事件发生时,所在区域的物理指标值高于冲击危险阈值,物理指标空间扫描辨识的危险区域与微震数据聚集区域基本一致;利用综合预测危险区域模型,对工作面回采期间危险区域进行迭代式预测,结果表明:冲击危险事件多发生在综合预测指标所预测的强冲击危险区域内,并随着回采期间微震数据的叠加,强冲击危险区域逐步集中,与冲击危险事件位置的重合度更高。综合预测指标预测效能总体高于单个物理指标,显著增强了精准预测冲击危险区域的能力。It is of great significance to accurately identify the rockburst hazard areas and give the hazard level and its evolution law for rockburst prevention and control.In this study,the method combining deformation localization with multivariate geophysical indicators spatial scanning is used to explore the precursor characteristics of microseismic in the area of high-energy microseismic events and track the dynamic evolution process of rockburst hazard areas.Based on the principle of deformation localization,the gradient significance indicator is used to identify the deformation localization areas and delineate the hazard area.The sliding window scanning method is used to study the spatial distribution characteristics of physical indicators such as b value,A(b)value and S value in the deformation localization areas.The b value,A(b)value,S value,ΔF and A(t)value corresponding to the high-energy microseismic events identified by the gradient significance index during excavation are used as the threshold values for classifying the rockbrust hazard level during the mining operation.The Bayesian network method is used to analyze the effectiveness of each physical indicator in predicting the hazard areas,and a comprehensive predicting hazard areas model is constructed to calculate the weight of physical indicators and obtain the comprehensive predicting indicators.The 513 working face is analyzed as an example.The results show that the geophysical indicators can identify the microseismic gathering signal and assess the hazard areas.Three microseismic events gathering areas are determined according to the measured data of 513 working face.The spatial scanning results of physical indicators and the gathering areas of microseismic data have the synchronization characteristics.When some high-energy microseismic events occur,the physical indicator value of the area is higher than the rockburst hazard threshold,and the hazard areas identified by the physical indicator spatial scanning is basically consistent with the gathering

关 键 词：冲击地压 变形局部化 危险区域 多元地球物理指标 动态辨识 

分 类 号：TD324[矿业工程—矿井建设]