Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO₃^- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

中文翻译：

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基于水化学成分的偏最小二乘判别分析模型在矿井多个含水层涌水源识别中的应用

矿井突水严重威胁煤矿安全生产。快速，准确地识别矿井突水水源对预防矿井水害具有重要意义。本文结合偏最小二乘判别分析（PLS-DA）和涌水化学成分，确定了矿井多个含水层涌水的来源。本研究选择了临hua矿区的仁楼煤矿，从三个含水层的54个水样中收集并测试了7种常规水化学成分，其中45个水样用于建立PLS-DA判别模型，以及9个用于测试预测效果。为了提高模型的准确性和预测能力，分层聚类分析方法用于消除七个不合格的水样，以减少由于数据不正确造成的误差。采用PCA和PLS-DA方法对剩余水样数据进行分析和处理，在PCA分析的基础上，利用剩余38个水样建立PLS-DA判别模型。使用排列和外部预测测试对模型进行了验证。研究表明：（1）PCA和PLS-DA方法都可以区分三种不同水源的水样，但PLS-DA的分类效果优于PCA，因为它可以增强水化学成分之间的差异。不同的水源。（2）PLS-DA判别模型的正确判别率高达100％，排列测试表明该模型不是过拟合的。外部验证发现该模型具有良好的稳定性和区分性。（3）HCO₃ ^-和总溶解固体（TDS）是影响基于变量重要性的投影（VIP）得分判别结果的最重要的标志物差的组合物。本研究建立的判别模型结合了主成分分析和多元回归分析的优势，为准确识别矿井的突水来源提供了一种新方法。