基于GIS的金属矿巷道突水预测  

Prediction of Water Inrush in Metal Mines Based on GIS

作　　者：谭文辉 张丽萍 李子建 TAN Wenhui;ZHANG Liping;LI Zijian(School of Civil and Resources Engineering,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学土木与资源工程学院,北京100083

出　　处：《湖南科技大学学报（自然科学版）》2023年第2期10-18,共9页Journal of Hunan University of Science And Technology：Natural Science Edition

基　　金：国家重点研发计划资助项目(2017YFC0804103)。

摘　　要：突水事故是地下开挖工程中常见的工程事故,为保障矿区的安全开采,以金属矿地下巷道为研究对象分析突水的主要影响因素,建立基于GIS和BP神经网络的脆弱性指数突水预测模型,并进行金属矿井突水预测.研究结果表明:金属矿巷道突水是多因素共同作用的结果,影响金属矿巷道突水危险性程度由大到小的因素依次是采动影响、断层、充水含水水压、巷道围岩防突性能和充水含水分布特性;巷道中严重的突水灾害易发于充水含水丰富且距离采场近的区域,并且与距断层的距离、充水含水水压和巷道围岩防突性能密切相关.Water inrush often occurs in underground excavation.In order to ensure the safety of mining area,the main influencing factors of water inrush are analyzed by taking underground roadway of metal mine as the research object.The water inrush prediction model of vulnerability index based on GIS and BP neural network is established,and the water inrush prediction of metal mine is carried out.The research shows that:(1)water inrush in metal mine is the result of multiple factors.The degree of impact on water inrush from big to small is mining,fault,water pressure,outburst performance of surrounding rock and distribution of water in rocks,and(2)serious water inrush is more prone to happen in the water-filled and water-rich rock mass close to the stope,and it is related to the distance from the fault and the water pressure and the anti-outburst performance of surrounding rock.

关 键 词：地理信息系统(GIS) BP神经网络 金属矿巷道 突水 预测 

分 类 号：TU451[建筑科学—岩土工程]