Zhao Fenghua, Guo Yuan, Sun Hongfu, Yang Meng. Fuzzy comprehensive discriminant method for complex limestone aquifers in Xinzhi coal mine[J]. Journal of Mining Science and Technology, 2019, 4(3): 195-203.
Citation: Zhao Fenghua, Guo Yuan, Sun Hongfu, Yang Meng. Fuzzy comprehensive discriminant method for complex limestone aquifers in Xinzhi coal mine[J]. Journal of Mining Science and Technology, 2019, 4(3): 195-203.

Fuzzy comprehensive discriminant method for complex limestone aquifers in Xinzhi coal mine

  • Received Date: 2019-03-13
  • Rev Recd Date: 2019-03-13
  • Publish Date: 2019-04-01
  • This paper summarized the water chemistry characteristics of three complex limestone aquifers in Xinzhi coal mine by systematically studying the water quality of 52 water samples.The improved fuzzy comprehensive discriminant method was used to discriminate the water samples of three limeston&aquifers in Xinzhi coal mine.The water chemistry characteristics of the K,limestone aquifer in the Taiyuan Formation of the Carboniferous in Xinzhi coal mine are obvious. The discriminating basis of K, limestone aquifer are SO,-Na type and the ratio of yca/Yso, of the water sample less than 0.5.Due to the complicalted geological structure , the uneven lithology of the stratum and the variable groundwater runoff conditions , the water quality parameters of the K,limestone aquifer of the Carboniferous Taiyuan Formation and the O,f limestone aquifer of the Ordovician Fengfeng Formation overlap each other and cannot be effectively identified by conventional hydrochemical methods.In this study , the percentage of constantion equivalent concentration , ratio coefficients of Yca/Yaand YuYc were used as the identification factors in the fuzzy comprehensive discriminant model , which organically integrated the water chemical  mechanism with the fuzzy mathematical principle.The constantion mass concentration is used as the identification factor in the traditional fuzzy comprehensive identification method.Through these improvements , the accuracy of the fuzzy comprehensive identification method has been improved from 75% to 96.2%.
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