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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.

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The coal roof of the lower seam of a bifurcated coal seam is always broken and is caused by the mining of the upper seam, which results in difficult support for the coal roof. This seriously affects the safe and efficient mining of the lower seam. Pre-grouting technology is often used to reinforce the regenerated roof in advance to improve its integrity of the roof. In this study, to provide a scientific basis for the layout of surface pre-grouting boreholes, the bifurcated coal seam at the Xutuan coal mine in the Huaibei mining area in China was analyzed. The height and depth of the damage in the coal roof and floor caused by upper seam mining were calculated using theoretical analysis and numerical simulation. The porosity and permeability of the goaf in different positions were calculated by numerical simulation, and the spatial distribution of the porosity and permeability and the slurry diffusion radius of different porosities were determined. Finally, the grouting section and layout of the pre-grouting boreholes were determined. Based on the design scheme, the drilling of the surface boreholes and the addition of the grouting were carried out. The accuracy of the theoretical analysis was verified by field drilling and grouting activity. The research results can provide a basis for the layout of surface pre-grouting boreholes and the selection of the grouting zone in areas of close-distance coal seam mining.

After coal mining, mining fissures develop, which may lead to an overlying aquifer and water inrush. Objectively and accurately evaluating and predicting the water abundance of the bottom aquifer of the Cenozoic are of great significance for ensuring safe mining of shallow coal seams and for the protection of water resources. The water richness evaluation index data has characteristics of high dimensionality, nonlinearity, and nonnormal distribution, and therefore, results often cannot objectively and truly reflect the water abundance of aquifers. To overcome these problems, a water abundance evaluation method is proposed that is based on the projection pursuit model. Taking the Cenozoic bottom aquifer in the Xutuan mine of Huaibei mining area, China, as a research object, a water abundance evaluation index system is constructed based on the law of sedimentary characteristics controlling groundwater. This system consists of four factors including aquifer thickness, sand and gravel layer thickness, number of sand and gravel layer, and ratio of sand and gravel content to total aquifer content. The projection pursuit comprehensive evaluation method is introduced to the water abundance evaluation of the aquifer. The method is used to optimize and solve the optimal projection direction, and the comprehensive projection value is calculated according to the optimal projection direction. The size of the comprehensive projection value is then used as basis for characterizing the water abundance degree of the Cenozoic bottom aquifer in the study area. Moreover, the Jenks natural breaks classification method is used to grade and evaluate the comprehensive projection value of water abundance. Finally, the evaluation results of water abundance are verified by the value of unit water inflow and the distribution of known water inrush points. The results show that it is feasible to employ the projection pursuit model for water abundance evaluation of mine aquifers, and optimal evaluation results can be obtained. The model projects high-dimensional data onto a one-dimensional subspace for data analysis and finds the optimal projection direction that can identify the data characteristics and mine the data to the greatest extent. Furthermore, this model avoids interferences by subjectivity and human factors and improves the scientificity and accuracy of the evaluation results. This study provides a new method and concept for the evaluation of aquifer water abundance involving multiple factors.