煤炭工程 ›› 2021, Vol. 53 ›› Issue (1): 123-127.doi: 10.11799/ce202101026

• 研究探讨 • 上一篇    下一篇

基于因子分析法的焦作矿区底板突水模型研究

盖秋凯,黄磊,赵霖   

  1. 中国矿业大学(北京)
  • 收稿日期:2019-10-29 修回日期:2020-03-10 出版日期:2021-01-20 发布日期:2021-04-27
  • 通讯作者: 盖秋凯 E-mail:1259619533@qq.com

Research on Floor Water Inrush Model of Jiaozuo Mining Area Based on Factor Analysis

  • Received:2019-10-29 Revised:2020-03-10 Online:2021-01-20 Published:2021-04-27
  • Contact: Kai QiuGai E-mail:1259619533@qq.com

摘要: 为研究焦作矿区工作面底板突水的主要影响因素和防治措施,选取地质构造λ1、煤层厚度λ2,煤层倾角λ3、工作面宽度λ4、开采深度λ5、含水层水头压力λ6、隔水层平均强度λ7、隔水层平均厚度λ8作为研究变量,建立了焦作矿区工作面底板突水的因子分析模型。研究结果表明:焦作矿区底板突水的8个变量可归纳为采矿赋存因子、采矿设计因子、技术可变因子|得出了模型加权综合得分大于等于0.95时则突水危险性很大,且需采取相应的技术措施的判别标准|该因子分析模型在赵固二矿11011工作面成功应用并验证了其正确性。该研究成果对焦作矿区及类似地质条件下采掘工作面底板是否需要注浆加固或采取其他防治措施提供了一定的参考意义。

关键词: 因子分析法, 焦作矿区, 底板突水, 突水模型, 判别标准

Abstract: In order to study the main influencing factors of water inrush from the working face of Jiaozuo mining area and how to take corresponding prevention measures,select geological structure λ1,coal seam thickness λ2,coal seam dip angle λ3,working surface width λ4, mining depth λ5, aquifer head pressure λ6, septumThe average intensity of water layer λ7 and the average thickness of water-repellent layer λ8 were used as research variables to establish a factor analysis model for water inrush from the working face of Jiaozuo mining area. The results show that the eight variables of water inrush from the floor of Jiaozuo mining area can be summarized as mining occurrence factor, mining design factor and technical variable factor. It is concluded that the model weighted comprehensive score ≥0.95 is very dangerous and needs to be taken. The discriminant standard of the corresponding technical measures; the factor analysis model was successfully applied and verified in the 11011 working face of Zhaogu No. 2 Mine. The research results of the thesis provide a certain reference significance for whether the floor of the mining face under the mining area and similar geological conditions needs grouting reinforcement or other prevention measures.