Water Abundance Comprehensive Evaluation of Coal Mine Aquifer Based on Projection Pursuit Model

Abstract

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.

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