中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (4): 125-132.doi: 10.16265/j.cnki.issn1003-3033.2021.04.017

• 安全工程技术 • 上一篇    下一篇

煤与瓦斯突出微震-瓦斯互动响应预警研究

隆能增1, 袁梅**1 教授, 王关亮2, 王清辉3, 许石青1, 李鑫灵1   

  1. 1 贵州大学 矿业学院,贵州 贵阳 550025;
    2 贵州玉马能源公司 马场煤矿,贵州 水城 553000;
    3 贵州水城矿业股份有限公司 汪家寨煤矿,贵州 六盘水 553000
  • 收稿日期:2021-01-10 修回日期:2021-03-08 出版日期:2021-04-28 发布日期:2021-12-20
  • 通讯作者: **袁 梅(1973—),女,贵州贵阳人,博士,教授,主要从事安全科学与工程方面的研究。E-mail:gutyuanmei@126.com。
  • 作者简介:隆能增 (1993—),男,广西崇左人,硕士研究生,研究方向为矿山安全与灾害防治。 E-mail:565825819@qq.com。
  • 基金资助:
    贵州省科技计划项目(黔科合支撑[2018]2789,[2019]2887)。

Research on early warning of coal and gas outburst microseismic-gas interactive response

LONG Nengzeng1,YUAN Mei1,WANG Guanliang2,WANG Qinghui3,XU Shiqing1,LI Xinling1   

  1. 1 Mining College,Guizhou University,Guiyang Guizhou 550025, China;
    2 Machang Coal Mine,Guizhou Yuma Energy Company, Shuicheng Guizhou 553000, China;
    3 Wangjiazhai Coal Mine, Guizhou Shuicheng Mining Co. Ltd., Liupanshui Guizhou 553000, China
  • Received:2021-01-10 Revised:2021-03-08 Online:2021-04-28 Published:2021-12-20

摘要: 为解决煤与瓦斯突出实时预警技术在现场应用误报率较高的问题,分析贵州省某矿掘进工作面煤与瓦斯突出主控因素和前兆特性,研究微震-瓦斯互动响应的实时预警机制,构建基于数据挖掘的煤与瓦斯突出智能预测模型,界定煤与瓦斯突出危险等级划分原则,建立煤与瓦斯突出危险性实时预警系统;并利用该矿掘进工作面实测数据预测煤与瓦斯突出危险性等级。研究结果表明:智能预测模型预测精度较高,预测结果与钻屑瓦斯解吸指标K1值及瓦斯压力值P的一致性较好,所建预警系统的预警等级与工作面实际煤与瓦斯突出危险情况基本相符。

关键词: 煤与瓦斯突出, 微震-瓦斯互动响应, 智能预测模型, 预警系统, 实时预警指标

Abstract: In order to solve problem of high false alarm rate of coal and gas outburst real-time early warning technology in the field, main controlling factors and precursor characteristics of coal and gas outburst in driving face of a mine in Guizhou Province were analyzed, and real-time early warning mechanism of microseismic-gas interactive response was studied. An intelligent prediction model for coal and gas outburst based on data mining was established, principles for dividing risk levels of coal and gas outbursts were defined, a real-time warning system for coal and gas outburst hazard was established. Coal and gas outburst risk grade was predicted by using measured data of heading face.The results show that prediction accuracy of intelligent prediction model is relatively high, and prediction results are in good agreement with drilling cutting-gas desorption index K1 value and gas pressure value P. Warning level of established warning system is basically consistent with actual coal and gas outburst risk on working face.

Key words: coal and gas outburst, microseismic-gas interaction response, intelligent prediction model, early warning system, real-time warning indicators

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