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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 method based on PCA for identifying water filling source in mine

  • 摘要: 采煤工作面回采过程中充水水源的多源不确定性是矿井水害防治的研究重点,采用水化学信息识别充水水源时,常难以明确界定各充水水源的特征型水质阈值,且现有大多数分析判别方法判别精度不高。基于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样本数据库,采用Piper三线图法和多因子法分析各充水水源的水质类型及训练样本数据库,建立了基于主成分分析的Fisher判别模型,并根据欧氏距离判别原则分析识别采空区涌水的充水水源。结果表明,充水水源主要为侏罗系采空积水,其次为底板灰岩水和顶板砂岩水;该判别模型判别精度可以达到99.9%, 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 multi-source uncertainty of water filling source in the process of coal winning in working face is the research emphasis of mine water disaster prevention. It is often difficult to define the characteristic water quality threshold of each water filling source when the water chemical information is used to identify the water source, and most of the existing analysis and discrimination methods results in low accuracy of discrimination. Based on the samples of potential water filling source from mine inflow in 8210 working face of Majiliang Coal Mine collected on site, the database of samples was established. The water quality types and the training sample database of each water filling source were analyzed by Piper three-line graph method and multi-factor method. Fisher discriminant water filling source model based on principal component analysis was established, and the types of water filling source in goaf at the sampling stage were analyzed and identified on the basis of Euclidean distance discrimination. The results show that the primary source of water filling is Jurassic goaf water, followed by floor limestone water and roof sandstone water. The discriminant precision of this model based on principal component analysis can reach 99.9%. The model is of great guiding significance for the field identification of water filling source in working face.

     

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