摘要
基于Bayes判别分析方法的基本思想,建立了煤层注水难易程度的Bayes判别分析模型。该模型选用煤层的埋藏深度、裂隙发育程度、孔隙率、湿润边角、饱和水分增值和坚固性系数6个指标作为判别因子,将煤层注水的难易程度分为3个等级作为Bayes判别分析的3个正态总体。以15组煤层注水实测数据作为训练样本,建立了Bayes判别模型。用回代判别的方法对15组实测数据进行判别分析,以验证模型的准确性,并将模型应用于工程实例中。研究结果显示,Bayes判别分析模型误判率较低,能更好的应用于实际工程中。
Based on the idea of the Bayes discriminant analysis method, the Bayes discriminant analysis model of the difficulty degree on coal seam water injection was established. Six indicators such as buried depth, cranny viability, porosity, wet rim angle, saturation moisture increment and firm coefficient of coal were used as discriminant factors in the model. The difficulty of the coal seam water in- jection is divided into three grades that were regarded as three normal population of the Bayes discriminant analysis. The Bayes discrim- inant model was set up through training of fifteen sets of in - situ data, each of the fifteen sets of samples was tested by using back sub- stitution method according to the Bayes discriminant analysis, and then the model was used in practical engineering. The results show that misjudgment rate of the Bayes discriminant analysis model is low and the model can be better applied to practical engineering.
出处
《煤矿安全》
CAS
北大核心
2016年第1期145-147,共3页
Safety in Coal Mines
基金
国家自然科学基金资助项目(41372301)
四川省教育厅重点资助项目(15zd2139)
西南科技大学杰出青年科技人才计划资助项目(13zx9109)
西南科技大学博士研究基金资助项目(12zx7118)
关键词
煤层注水
Bayes判别分析
注水难易程度
工程应用
判别分析法
coal seam water injection
Bayes discriminant analysis
difficulty degree of water injection
engineering application
dis-criminant analysis method
