摘要
结合主成分分析和贝叶斯(Bayes)判别简化构建突水水源识别模型,水样变量因子选取Ca^(2+)、Na^++K^+、Mg^(2+)、HCO_3^-、Cl^-、SO_4^(2-)六个指标。采用潘二矿新生界松散层、煤系砂岩以及太原组灰岩中的水质分析资料作为训练样本和预测样本,其中,训练样本24个,预测样本11个,判别结果表明:松散层水正确率为81. 8%,砂岩水正确率为83. 3%,灰岩水正确率为85. 7%,整体正确率为83. 3%,判别结果可信度高。同时,将主成分分析和贝叶斯结合突水识别模型与贝叶斯模型比较表明利用主成分分析和贝叶斯结合的模型能有效消除冗余信息,使判别结果更加快速准确。
The six index of Ca^2+、Na^+ +K^+、Mg^2+、HCO3^- 、Cl^-、SO4^2- were selected as variables water samples. Water inrush identification model was established combining principal component analysis and Bayesian discriminant simplification. The water quality analysis data of the Cenozoic loose beds,coal-serial sandstone and Taiyuan Formation limestone in Panji No. 2 Mine were used as training samples and prediction samples,including 24 training samples and 11 prediction samples. The results show that the correct rate of water in the loose layer is 81. 8%,the correct rate of sandstone water is 83. 3%,the correct rate of limestone water is 85. 7%,the overall correct rate is 83. 3%,and the reliability of the discriminant results is high. At the same time,the principal component analysis and Bayesian combination water inrush recognition model compared with Bayesian model shows that the combination of principal component analysis and Bayesian model can effectively eliminate redundant information and make the discriminant results more rapid and accurate.
作者
琚棋定
胡友彪
张淑莹
JU Qi-ding;HU You-biao;ZHANG Shu-ying(School of Earth and Environment,Anhui University of Science and Technology,Huainan 232001,China)
出处
《煤炭工程》
北大核心
2018年第12期90-94,共5页
Coal Engineering
基金
国家自然科学基金项目(41472235)
关键词
主成分分析法
贝叶斯判别
矿井突水
水源判别
principal component analysis
Bayes discrimination analysis
mine water inrush
identification ofwater source
