岩爆烈度分级预测中贝叶斯判别分析.pdfVIP

SeriesNo. 407 金 属 矿 山 总第407期 M ay 2010 M ETALM INE 2010年第 5期 岩爆烈度分级预测中的贝叶斯判别分析* 赵国彦 刘 强 刘 超 ( 中南大学 ) , , W , / / et c c t ; 4 4; 14 , , , , , , 岩石力学 岩爆 烈度分级 预测 贝叶斯模型 B ayes D iscrim inan t Analy sis for C lassification and P red iction of Rockburst In tensity Zhao Guoyan L iu Q iang L iu hao (Central South University) A bstrac t In view of many uncertain elements in classification and prediction of rockburst intensity, the evaluation model for classification and prediction of rockburst intensity is established by Bayes discrmi inant analysis. According to the characteristic and cause of rockburst, three factors of the stress coefficient of rock, the brittleness coefficient of rock and the elastic energy index of rock are selected as the discrmi inant factors. And the intensity of rockburst is divided into 4 grades, denoted as none, inferior, medium and intense, which are taken as the four normal populations in Bayes discrmi inant analy sis. The classification discrmi ination is carried outbased on the initial data for rockburst intensity analysis of 14 rock pro jects around the w orld. Bayes linear discrmi inant function is obtained through the training samples, which is then used to calculate the Bayes function value of the evaluating samples. M eanwhile, the population corresponding to the maxmi um function value is used to judge which population the evaluating sample belongs to. In order to verify the effectiveness and feasibility of themodel, the cases of Q inling Tunnel and Fankou LeadZ incM ine are analyz