王爱文,李超,潘一山,等. 冲击危险区域多元地球物理指标动态辨识方法及其应用[J]. 煤炭学报,2024,49(6):2573−2588. doi: 10.13225/j.cnki.jccs.2024.0024
引用本文: 王爱文,李超,潘一山,等. 冲击危险区域多元地球物理指标动态辨识方法及其应用[J]. 煤炭学报,2024,49(6):2573−2588. doi: 10.13225/j.cnki.jccs.2024.0024
WANG Aiwen,LI Chao,PAN Yishan,et al. Dynamic identification method for rockburst hazard areas based on multivariate geophysical indicators and its application[J]. Journal of China Coal Society,2024,49(6):2573−2588. doi: 10.13225/j.cnki.jccs.2024.0024
Citation: WANG Aiwen,LI Chao,PAN Yishan,et al. Dynamic identification method for rockburst hazard areas based on multivariate geophysical indicators and its application[J]. Journal of China Coal Society,2024,49(6):2573−2588. doi: 10.13225/j.cnki.jccs.2024.0024

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

  • 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bA(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bA(b)、S、∆FA(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。

     

    Abstract: 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, ∆F 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 areas of microseismic data. The integrated prediction model is used to predict the hazard area during the mining period of the working face. The results show that the rockburst hazard events mostly occur in the strong high hazard areas predicted by the integrated prediction indicator. With the superposition of microseismic data during the mining period, the high rockburst hazard areas is further concentrated, and the overlap degree with the high hazard event location is higher. The prediction efficiency of the integrated prediction indicator is generally higher than that of single physical indicator, which significantly enhances the ability to accurately predict the rockburst hazard areas.

     

/

返回文章
返回