复杂采空区危险辨识的贝叶斯判别方法及应用(英文)

提出了复杂采空区危险程度辨识的贝叶斯判别方法。基于多元判别分析理论,将贝叶斯判别方法应用于金属矿山采空区危险程度的预测判别问题中,建立了相应的贝叶斯判别分析模型。该模型选用岩石单轴抗压强度、岩石弹性模量、岩石质量指标、矿柱面积比率、矿柱宽高比、矿体埋藏深度、采空区体积、矿体倾角和采空区面积9项指标作为判别因子,将采空区的危险性等级分为4级;以40个采空区实测数据作为学习样本进行训练,建立相应判别函数对待判样本进行分类。研究结果表明,贝叶斯判别模型的学习精度很高,回判估计的误判率为0.025。利用学习后的模型对某金属矿山采空区实例进行了稳定性判别,判别结果和实际情况相符。

Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application

A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented.Nine factors influencing the stability of goaf risky,including uniaxial compressive strength of rock,elastic modulus of rock,rock quality designation(RQD),area ratio of pillar,ratio of width to height of pillar,depth of ore body,volume of goaf,dip of ore body and area of goaf,were selected as discriminant indexes in the stability analysis of goaf.The actual data of 40 goafs were used as training samples to establish a discriminant analysis model to identify the stability of goaf.The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process.The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.