CAJ | 학술논문

以湖北省鄂州程潮铁矿和黄石大冶铁矿为例，利用GIS空间分析功能对研究区数据进行提取分级、赋值统计及归一化等处理，构建了包括高程、坡度、地层、地下开采点的分布密度、相距最近地下开采点的距离、开采厚度与深度比值、蚀变接触带缓冲区、地下水深度以及地表地物类型的矿区采空塌陷易发性评价指标数据集；借助IDL语言调用Matlab神经网络工具箱，将研究区2011和2012年的指标数据集作为输入数据，塌陷易发性作为期望输出，建立基于BP神经网络的矿区采空塌陷易发性预测模型；通过选取并优化训练样本，实现对2013年矿山塌陷易发性的预测。结果表明，高易发区及以上的区域包含89.91%的采空塌陷，随着易发等级的提高，采空塌陷面积占易发等级面积比也随之增大；采空塌陷的分布具有明显的地带性，高易发区基本沿着岩体与围岩的接触带分布。模型解决了塌陷预测中的非线性映射问题，预测结果与实际调查情况基本吻合。BP 神经网络模型与GIS技术相结合预测矿区采空塌陷的易发性具有可行性。
이호북성악주정조철광화황석대야철광위례，이용GIS공간분석공능대연구구수거진행제취분급、부치통계급귀일화등처리，구건료포괄고정、파도、지층、지하개채점적분포밀도、상거최근지하개채점적거리、개채후도여심도비치、식변접촉대완충구、지하수심도이급지표지물류형적광구채공탑함역발성평개지표수거집；차조IDL어언조용Matlab신경망락공구상，장연구구2011화2012년적지표수거집작위수입수거，탑함역발성작위기망수출，건립기우BP신경망락적광구채공탑함역발성예측모형；통과선취병우화훈련양본，실현대2013년광산탑함역발성적예측。결과표명，고역발구급이상적구역포함89.91%적채공탑함，수착역발등급적제고，채공탑함면적점역발등급면적비야수지증대；채공탑함적분포구유명현적지대성，고역발구기본연착암체여위암적접촉대분포。모형해결료탑함예측중적비선성영사문제，예측결과여실제조사정황기본문합。BP 신경망락모형여GIS기술상결합예측광구채공탑함적역발성구유가행성。
By using GIS, spatial analyses, including extraction, classification, valuation, statistics and normalization, were made with the data of the study areas in Chengchao Iron Mine and Daye Iron Mine of Hubei province. An index data set, including elevation, slope, strata, the recent distance from underground mines,the distribution density of underground mines, the ratio of mining thickness and depth, the buffer of alteration contact zone, groundwater depth, and the surface feature types, was constructed to assess the susceptibility of collapse in mining area. With IDL language to call Matlab neural network toolbox, the index data set of the study area in 2011and 2012 was used as input data, and the susceptibility was used as expected output. So the model based on BP neural network for predicting the susceptibility of underground mining collapse was constructed. By selecting and optimizing the training sample, this model realized the prediction for the susceptibility of collapse in 2013. The results indicated that the underground mining collapse area accounted for 89.91% of the total area highly susceptible to collapse;with the increase of the susceptibility level, the ratio of the underground mining collapse area to the susceptible area increased; the distribution of underground mining collapse was of obvious zonality, and the high susceptibility area distributed basically along the contact zone between the rock mass and the surrounding rock. The model solved the problem of nonlinear mapping in collapse prediction, as the predicted results were in accord with the actual survey. The results show that BP neural network model and GIS technology for evaluating the susceptibility of underground mining collapse would have a certain feasibility.