樊鑫,程建远,栗升,等. 煤矿微震监测系统在回采工作面顶板水害防治中的应用[J]. 煤田地质与勘探,2024,52(6):115−127. DOI: 10.12363/issn.1001-1986.24.03.0230
引用本文: 樊鑫,程建远,栗升,等. 煤矿微震监测系统在回采工作面顶板水害防治中的应用[J]. 煤田地质与勘探,2024,52(6):115−127. DOI: 10.12363/issn.1001-1986.24.03.0230
FAN Xin,CHENG Jianyuan,LI Sheng,et al. Application of microseismic monitoring system for coal mines to the prevention and control of water disasters on working face roofs[J]. Coal Geology & Exploration,2024,52(6):115−127. DOI: 10.12363/issn.1001-1986.24.03.0230
Citation: FAN Xin,CHENG Jianyuan,LI Sheng,et al. Application of microseismic monitoring system for coal mines to the prevention and control of water disasters on working face roofs[J]. Coal Geology & Exploration,2024,52(6):115−127. DOI: 10.12363/issn.1001-1986.24.03.0230

煤矿微震监测系统在回采工作面顶板水害防治中的应用

Application of microseismic monitoring system for coal mines to the prevention and control of water disasters on working face roofs

  • 摘要: 【目的】随着煤矿开采深度增加,工作面顶板水害风险日益突出,制约了我国煤矿绿色安全高效开采,亟需新型顶板水害监测预警技术。探索井−地联合微震监测系统在回采工作面顶板水害防治中的应用,实现顶板水害风险的透明化智能监测预警。【方法】在工作面上方地表和井下两侧巷道同步布置微震传感器阵列,增加垂向采集空间,提高震源定位精度;提取监测数据时频域特征构建样本数据集,利用支持向量机算法实现微震事件智能识别。【结果和结论】以陕西彬长矿区某煤矿工作面为例,成功识别出3个煤层顶板应力集中区域,与电阻率监测成果吻合,显示导水裂隙带高达160 m,波及含水层,显示出回采期间顶板导水通道动态发育全过程。该井−地联合微震监测系统能够高精度识别微震事件,为回采工作面顶板水害风险监测预警提供新技术手段。研究有助于推进微震监测技术在煤矿顶板水害等领域的智能化应用,促进煤矿开采的安全绿色高效发展。

     

    Abstract: Objective Increasing coal mining depth in coal mines has caused increasingly prominent risks of water disasters on working face roofs, which restrict the green, safe, and efficient coal mining in China’s coal mines. Therefore, there is an urgent need for new technologies for the monitoring and early warning of these water disasters. Methods This study explored the application of the joint well-ground microseismic monitoring system to the prevention and control of water disasters on working face roofs, achieving transparent, intelligent monitoring and early warning of water disaster risks. In this system, microseismic sensor arrays are arranged on the surface above a working face and along the underground roadways on both sides of the working face to increase the vertical data acquisition space and seismic source location accuracy. Furthermore, the time- and frequency-domain characteristics of the monitoring data are extracted to construct a sample data set, and the intelligent identification of microseismic events is achieved using the support vector machine (SVM) algorithm. Results and Conclusions Taking a working face in a certain coal mine within the Binchang mining area in Shaanxi as an example, the joint well-ground microseismic monitoring system allowed for the successive identification of three stress concentration zones on the coal seam roof, coinciding with the resistivity monitoring results. The system also identified a hydraulically conductive fracture zone with a height reaching up to 160 m, which spread to aquifers, revealing the whole dynamic development process of hydraulically conductive channels on the roof during mining. The joint well-ground microseismic monitoring system can accurately identify microseismic events, providing a new technology for the monitoring and early warning of water disaster risks on a working face roof. This study helps promote the intelligent applications of microseismic monitoring technology in fields such as the prevention and control of water disasters on coal mine roofs, thus promoting safe, green, and efficient coal mining.

     

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