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基于PCA的Fisher多元统计方法识别矿井充水水源

Fisher Multivariate Statistical Discriminant Method based on Principal Component Analysis for Identifying Water-filled Source in Goaf of Mine: a Case Study from the Majiliang Mine, Northern China

  • 摘要: 根据现场采集的马脊梁煤矿8210工作面矿井涌水可能充水水源样本建立水源类型水质样本数据库,采用Piper三线图分析法和多因子分析法整理分析了各充水水源的水质类型及训练样本数据库;应用基于主成分分析方法的Fisher多元统计理论,建立了基于主成分分析的Fisher判别充水水源模型并根据欧氏距离判别原则分析识别采样阶段采空区涌水的充水水源,首要为侏罗系采空水,其次为底板灰岩水和顶板砂岩水。此次基于主要成分分析的费希尔判别模型判别精度可以达到99.9%。说明该方法对于采煤工作面矿井涌水充水水源的现场识别具有重要指导意义。

     

    Abstract: The database of water source types and water quality samples is established based on the samples collected from 8210 working face of Majiliang coal mine. In this paper, the water quality types and the training sample database of each waterfilled source were analyzed by Piper three-line graph analysis method and multi-factor analysis method, and Fisher multivariate statistical theory based on principal component analysis method was applied, fisher discriminant water-filling source model based on principal component analysis is established, and the water-filling source of gushing water in mined-out area at sampling stage is identified according to Euclidean distance discriminant principle, the second is floor limestone water and roof sandstone water. The discriminant precision of Fisher discriminant model based on principal component analysis can reach 99.9%. Therefore, the application of this method has an important guiding significance for the on-site identification of mine water inrush and filling source in coal mining face.

     

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