摘要
为消除采空区给铁矿山人员和生产带来的巨大安全隐患,对采空区精准探测方法进行了研究。在深入分析高密度电阻率法采空区探测工作原理的基础上,建立了采空区探测模型,据此总结出采空区在电阻率剖面图中所具有的高阻异常和电阻率突变的识别特征。以五台—吕梁地区某大型铁矿山采空区为例,进行了精准探测。根据采空区识别标志圈定了采空区异常的分布范围,采空区在探测成果图中有较明显的反映,电阻率异常特征与解译标志基本符合;钻探验证表明,通过高密度电阻率法可有效识别出采空区;结合采空区三维激光扫描成果,建立了采空区三维模型,精确地获取了采空区的空间特征信息。研究结果表明:采用高密度电阻率法圈定异常、钻探验证和三维激光扫描建模的综合探测方法可对铁矿山采空区进行高效、经济、精准探测。
In order to eliminate the great potential safety hazard that the goaf of iron mine brought to the iron mine workers and production,the accurate detection method of the goaf in a iron mine is studied.Based on anlyzing the working principle of the goaf detection by adopting the high density resistivity method,the goaf detection model is established,the identification characteristics of high resistivity anomalies and resistivity mutation of the goaf that is shown on the resistivity profile.Taking the goaf of a large iron mine of Wutai-Luliang region as an example,the accurate detection method of the goaf in a iron mine is studied.The distribution range of the goaf anomalies is delineated based on the goaf identification marks,the goaf is shown in the detection results figures obviously,the resistivity anomaly characteristics is consistent with interpretation marks,the drilling verification results show that the goaf can be identified effectively by adopting the high density resistivity method.Based on the three-dimensional laser scanning results of the goaf,three-dimensional model of the goaf is established,the spatial characteristics informatin of goaf is obtained accurately.The above study results show that the goaf of iron mine can be detected efficiently,economically and accurately by using the comprehensive detection method of adopting high density resistivity method to delineate anomalies,drilling verification and three-dimensional laser scanning modeling.
出处
《金属矿山》
CAS
北大核心
2016年第5期196-199,共4页
Metal Mine
基金
国家重点基础研究发展计划(973计划)项目(编号:2012CB416800)
国家自然科学基金项目(编号:41372098)
关键词
采空区
精准探测
高密度电阻率法
三维激光扫描
采空区探测模型
Goaf
Accurate detection
High density resistively method
Three-dimensional laser scanning
Detection model of goaf