Abstract
In order to explore the law of groundwater evolution, the water source connection between faults and aquifers and the main sources of mine water inrush in the deep mining area of Yangcheng Coal Mine in Jining City, 40 groups of hydrochemical samples were collected and analyzed by Piper Diagram and Durov Diagram. The results showed that the fluidity of groundwater developing to the deep became weaker, the value of total dissolved solids (TDS) became larger. So, the roof and floor of coal seam were more similar in water quality types due to the conduction of faults. Using principal component analysis (PCA) to the raw data, two principal components were extracted, and the principal component scores were used as clustering variables for hierarchical cluster analysis (HCA), 5 groups of abnormal water samples were eliminated and 3 clustering groups M1, M2 and M3 were obtained from the other water samples on the tree diagram. The results showed that the combination of HCA and hydrochemical analysis was more effective in screening water samples, and the 3 clustering groups could be qualified samples to represent 3 major aquifers (Taiyuan Formation limestone aquifer, Shanxi Formation sandstone aquifer and Ordovician limestone aquifer). Finally, taking M1, M2 and M3 as grouping variables, the discriminant functions f 1 , f 2 and f 3 of the 3 aquifers were obtained, the results of stepwise discrimination analysis (SDA) showed that the discrimination model established by using 25 groups of standard water samples could discriminate the known water samples with the correct rate of 96%, 10 groups of unknown water samples collected at the fault are identified as Taiyuan Formation limestone water samples, which was consistent with the classification results of HCA, proving that the water inrush of fault DF53 was from Taiyuan Formation limestone aquifer, while the fault had little influence on Ordovician limestone aquifer.
Highlights
The problem of mine water inrush has been a serious limitation of the construction and development of the coal mine, if no measures are taken to prevent and control the waterabundance aquifer at the initial stage of mining, it is easy to conduct the aquifer or generate large fracutures without knowing it during the mining process, causing confined water in the water-abundance aquifer to flow into the working face, stopping work or production in light cases, even causing heavy casualties in heavy cases [1]
The isotope method could be used to track the source of mine water and determine the connectivity of groundwater aquifers based on the composition of mine water and the ratio of measured isotope [5, 6]; The theoretical formula of nonlinear mathematics was used to calculate the correlation between sample indicators and data, and the respective discriminant models and evaluation results were obtained, such as grey system theory [7, 8], artificial neural network [9, 10], fuzzy mathematics [11, 12], which have good effects on distinguishing the source of mine water
The formation of HCO3- was initially due to the hydrolysis of potassium feldspar and albite in the coal measures strata in the deep strata, which increased the Na++K+ content in the groundwater
Summary
The problem of mine water inrush has been a serious limitation of the construction and development of the coal mine, if no measures are taken to prevent and control the waterabundance aquifer at the initial stage of mining, it is easy to conduct the aquifer or generate large fracutures without knowing it during the mining process, causing confined water in the water-abundance aquifer to flow into the working face, stopping work or production in light cases, even causing heavy casualties in heavy cases [1]. Water source identification methods have developed rapidly. Geofluids theoretical comprehensive method to identify water sources [2,3,4]. The isotope method could be used to track the source of mine water and determine the connectivity of groundwater aquifers based on the composition of mine water and the ratio of measured isotope [5, 6]; The theoretical formula of nonlinear mathematics was used to calculate the correlation between sample indicators and data, and the respective discriminant models and evaluation results were obtained, such as grey system theory [7, 8], artificial neural network [9, 10], fuzzy mathematics [11, 12], which have good effects on distinguishing the source of mine water
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