您的位置:首页  > 论文页面

金属矿隐覆采空区探测及其边界智能预测

发表时间:2011-09-30  浏览量:1152  下载量:625
全部作者: 刘志祥,赵国彦,周士霖,宫凤强
作者单位: 中南大学资源与安全工程学院
摘 要: 针对栾川钼矿和大宝山铜矿隐覆采空区特征,提出高密度电法与地震映像法相结合并辅以钻孔验证的金属矿隐覆采空区探测方法,工程应用结果表明:该方法经济可行,探测精度高。采用分形理论研究采空区分形特征,研究数据显示:采空区分形盒维数反映了采空区复杂程度或不规则程度。用采空区分形盒维数和工程探测数据,建立采空区上部边界和下部边界的神经网络智能预测模型,该模型根据采空区已有边界特征,智能预测未知或仪器测量不到的采空区边界,节省了隐覆采空区探测成本,提高了探测效率。
关 键 词: 采矿工程;隐覆采空区;智能预测模型;分形;神经网络
Title: Detection of metal mine hidden goaf and intelligent prediction of boundary
Author: LIU Zhixiang, ZHAO Guoyan, ZHOU Shilin, GONG Fengqiang
Organization: School of Resources and Safety Engineering, Central South University
Abstract: According to the characteristics of hidden goaf of Luanchuan Molybdenum mine and Dabaoshan Copper mine, a new detection method, high-density electrical method combined with the seismic imaging method, aided with the drilling verification metal hidden goaf, is put forward. Results of engineering application show that this method is of high precision, economic and feasible. Using fractal theory to study fractal characteristics of goaf, the data display, fractal box dimension of goaf reflects the degree of complexity and irregularity. With fractal box dimension and engineering detection data of goaf, neural network intelligent forecast model is established for the upper boundary and lower boundary of goaf. According to the existing characteristic of goaf boundary, the model can intelligent prediction goaf boundary, which is unknown or instrument can not measure, and it saves the cost of hidden goaf exploration and improves the efficiency of detection.
Key words: mining engineering; hidden goaf; intelligent prediction model; fractal; neural network
发表期数: 2011年9月第18期
引用格式: 刘志祥,赵国彦,周士霖,等. 金属矿隐覆采空区探测及其边界智能预测[J]. 中国科技论文在线精品论文,2011,4(18):1658-1665.
 
0 评论数 0
暂无评论
友情链接