Divisions based on groundwater chemical characteristics and discrimination of water inrush sources in the Pingdingshan coalfield

被引:1
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作者
Xinyi Wang
Hongying Ji
Qi Wang
Xiaoman Liu
Dan Huang
Xiaoping Yao
Guoshen Chen
机构
[1] Henan Polytechnic University,Institute of Resources and Environment
[2] China Pingmei Shenma Group,College of Earth Science
[3] Collaborative Innovation Center of Coalbed Methane and Shale Gas for Central Plains Economic Region of Henan Province,School of Civil Engineering
[4] University of Chinese Academy of Science,undefined
[5] Chongqing Nengke Engineering Exploration Co.,undefined
[6] LTD,undefined
[7] Henan Polytechnic University,undefined
来源
关键词
Geological characteristics; Key groundwater chemical components; Distribution law; Detail division; Water inrush source; Discriminant models;
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学科分类号
摘要
Within the Pingdingshan coalfield, the spatial distribution characteristics of key groundwater chemical components were studied in four aquifers that influence coal-mining operations. Thirty-six water samples were collected from the four aquifers, which were divided into smaller units. Discriminant models of mine-water inrush sources were developed for the three divisions I, III, and IIII, and engineering verification was conducted. The results showed that the main indices influencing groundwater chemical characteristics were Na++K+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− in the Pingdingshan coalfield with regional variations according to faults and synclines. The Guodishan fault was the primary groundwater chemical division structure, which divides the Pingdingshan coalfield into two primary groundwater chemically characteristic units, divisions I and II. The Likou syncline was the secondary groundwater chemically characteristic structure, which divides division II into two secondary groundwater chemically characteristic units: divisions III and IIII. Discriminant models of water inrush sources in divisions I, III, and IIII (12 discriminant functions) were developed, and engineering verification showed that discrimination accuracy for 16 groundwater samples in four aquifers of three divisions was 93.75 %.
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