基于BP神经网络的矿井小构造预测  

Forecast of Small Structure for Mine Based on BP Neural Networks

作　　者：徐东晶[1,2] 施龙青[1,2] 邱梅[1,2] 孙祺[1,2] 刘磊[1,2] 

机构地区：[1]山东省沉积成矿作用与沉积矿产重点实验室,山东青岛266590 [2]山东科技大学地质科学与工程学院,山东青岛266590

出　　处：《煤矿安全》2013年第2期50-52,56,共4页Safety in Coal Mines

基　　金：国家自然科学基金资助项目(41072212);国家自然科学基金重点项目资助项目(51034003);山东省自然科学基金重点资助项目(ZR2011EEZ002);山东科技大学研究生科技创新基金资助项目(YCA120318)

摘　　要：阐述了矿井小断层水平延展长度预测BP网络模型的构建、训练及模拟方法,在总结小断层落差、断层的走向、断层的倾角及断层的倾向等影响因素的基础上,结合赵官井田主采7#煤层典型样本数据,运用Matlab软件来建立网络预测模型,并对该煤层2713和2712 2个工作面的小断层水平延展长度进行预测,发现该网络模型的预测结果与实测结果更接近。the paper elaborates the building, training and simulation method of BP networks which can predict the horizontal extending length of small faults in coal seam. Based on the summary of the influence factors including drop height for small scale faults, fault strike and fault dip, dip direction and other factors, combined with the typical sample data of 7＂ coal seam which is mainly in Zhaoguan Mine Field, using Matlab software to construct a new network prediction model, and the horizontal extension length of small faults in working surface No. 2713 and No. 2712 of coal seam are predicted, it is obvious that the measured results got by using the model sys-tem is basically agree with the actual measurement result.

关 键 词：BP神经网络 构造预测 MATLAB软件 

分 类 号：TD163[矿业工程—矿山地质测量]