Multivariate statistical analysis of coal mine groundwater chemistry characteristics: A case study of Guqiao Coal Mine, Huainan, China

Analyzing coal mine groundwater chemistry characteristics and revealing its causes of formation is an important task of discriminating mine water-inrush source and water damage prevention and control. Nonlinear principal component analysis (NLPCA) and cluster analysis methods were employed to analyze water chemistry characteristics of mine aquifers. Ca2+, Mg2+, Na+ + K+, HCO3-, Cl- and SO(4)(2-)of water samples from each aquifer were analyzed by the two methods. Nonlinear principal component analysis (NLPCA) revealed: the relationship between aquifer water samples which can be explained by possible hydraulic connection between aquifers, the relationship between the six water quality indicators which can reflect their interaction, and the relationship between samples and water quality indicators which reveals the characteristics of each aquifer water quality and reflects the results of waterrock interaction. Water samples and variables were also treated with cluster analysis, and the results conform with the results of NLPCA on the whole.