煤层注水难易程度的Bayes判别分析法
Bayes Discriminant Analysis Method for Classifying Difficulty Degree of Coal Seam Water Injection
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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。Abstract: 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 injection is divided into three grades that were regarded as three normal population of the Bayes discriminant analysis. The Bayes discriminant 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 substitution 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.
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