朱南南, 张浪, 范喜生, 舒龙勇. 基于微震监测技术的突出危险性预警模型[J]. 煤矿安全, 2018, 49(2): 155-158.
    引用本文: 朱南南, 张浪, 范喜生, 舒龙勇. 基于微震监测技术的突出危险性预警模型[J]. 煤矿安全, 2018, 49(2): 155-158.
    ZHU Nannan, ZHANG Lang, FAN Xisheng, SHU Longyong. Early Warning Model of Outburst Danger Based on Micro-seismic Monitoring Technique[J]. Safety in Coal Mines, 2018, 49(2): 155-158.
    Citation: ZHU Nannan, ZHANG Lang, FAN Xisheng, SHU Longyong. Early Warning Model of Outburst Danger Based on Micro-seismic Monitoring Technique[J]. Safety in Coal Mines, 2018, 49(2): 155-158.

    基于微震监测技术的突出危险性预警模型

    Early Warning Model of Outburst Danger Based on Micro-seismic Monitoring Technique

    • 摘要: 合理的微震预警指标及预警模型是利用微震监测技术进行突出危险性预测的依据。对现场实测微震监测数据进行对比分析,结果表明:微震能率与事件率相比更能反映实际情况,可以把能率作为主要指标,而事件率作为辅助指标,两者综合判断掘进面前方的突出危险性。基于微震预警指标近似服从正态分布和国外俄罗斯的微震预警判据,提出了微震预测突出的综合预警模型,2条判据2个方面,相互补充,并通过大量的现场数据,验证了其准确性。同时现场突出前后微震监测数据表明:突出事故发生时刻对应预警指标的最大值;突出事故发生之前,出现了10 d左右的平静期。

       

      Abstract: Reasonable micro-seismic early warning indicators and early warning model are the basis of coal and gas outburst prediction by using micro-seismic monitoring technology. By comparing the seismic monitoring data measured in field, the results show that the energy rate of micro-seismic can reflect reality better than event rate. The energy rate can be used as the main index, and the event rate can be used as the auxiliary index, both of which comprehensively evaluate the outburst danger in front of the driving face. Based on the approximate normal distribution of micro-seismic warning indicators and the Russian micro-seismic warning criterion, the comprehensive early warning model of micro-seismic prediction outburst is proposed, the two criteria of which contain two aspects and complement each other. Through a large number of field data, the accuracy of the comprehensive early warning model is verified. In addition, micro-seismic monitoring data before and after the outburst shows that the maximum value of the early warning index appears while the outburst occurs, and there is the quiet period of about 10 days before the accident.

       

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