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Application of distance discriminant analysis method to headstream recognition of water-bursting source

机译：距离判别分析方法在水爆源上识别下的应用

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Based on the principle of Mahalanobis distance discriminant analysis (DDA), a distance discriminant analysis model of headstream recognition is established, including six indexes reflecting the mine water-bursting; Na~+ + K~+, Cl~-, Ca~(2+), Mg~(2+), (HCO_3)~-, (SO_4)~(2-). Linear discriminant functions are obtained through training the initial data for headstream recognition of mine water-bursting of 35 groups in Jiaozuo mining area. After the DDA model is trained, the ration of mistake distinguish is considerably low zero. In order to verify the effectiveness and feasibility of the model, the cases of other aquifer of groundwater in Jiaozuo mining area are analyzed using the proposed method, and the obtained results are also compared with the results of quantification theory model, support vector machines model and practical conditions. The results show that the results predicted by DDA model are both in good agreement with the practical conditions and the results obtained from quantification theory model and support vector machines model. The method offers a new way in headstream recognition of mine water-bursting.
机译：基于Mahalanobis距离判别分析（DDA）的原理，建立了一个距离判别分析模型，包括六指标，反射矿井水爆; na〜+ + k〜+，cl〜 - ，ca〜（2+），mg〜（2+），（hco_3）〜 - ，（so_4）〜（2-）。通过培训焦作矿区35组矿山水爆地雷识别的初始数据来获得线性判别函数。培训DDA模型后，错误的差异区分零点相当低。为了验证模型的有效性和可行性，使用该方法分析了焦作挖掘区域的其他地下水含水层的病例，并将获得的结果与量化理论模型的结果进行了比较，支持向量机模型和实际条件。结果表明，DDA模型预测的结果与实际条件和从量化理论模型获得的结果进行了良好的一致性，以及支持向量机模型。该方法提供了一种新的矿井爆破头部识别的新方法。

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