Jiang Qilin, Liu Qimeng, Liu Yu, Chai Huichan, Zhu Jingzhong
School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, China.
State Key Laboratory Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, 232001, China.
Heliyon. 2024 Feb 27;10(5):e26925. doi: 10.1016/j.heliyon.2024.e26925. eCollection 2024 Mar 15.
This study aims to accurately identify mine water sources and reduce the hazards caused by water inrush accidents in coal mines. Taking the Gubei coal mine as an example, the water quality results of the water samples from the Cenozoic unconsolidated aquifer, Permian sandstone fracture aquifer, and Carboniferous Taiyuan Formation limestone karst fracture aquifer in the mine area were tested, and K+Na, Ca, Mg, Cl, SO, HCO, TDS (Total Dissolved Solids), and pH were selected as the main indicators to study the water chemistry characteristics of the aquifer through water chemistry component analysis, major ion content analysis, Piper trilinear analysis, and correlation analysis. Thirty-five groups of water samples were randomly selected and imported into SPSS software for factor analysis (FA) and downsized to three main factors as the input variables of the artificial neural network model. The particle swarm optimization (PSO) code was written based on the MATLAB platform to improve the self-adjustment weights and acceleration factors for optimizing the initial weights and thresholds of the Back-Propagation (BP) neural network. The training and prediction samples were learned in the ratio of 8:2, and the recognition results were compared with the traditional BP neural network model. Results showed that the groundwater of the Gubei coal mine demonstrated a water quality vertical zoning pattern, and the chemical composition was dominated by cation K+Na and anion Cl. The FA-PSO-BP neural network model has a higher accuracy of water source discrimination compared with the cluster analysis and the FA-BP neural network model. The FA-PSO-BP neural network model is worthy of further application in the problem of water source identification in mine water inrush.
本研究旨在准确识别矿井水源，减少煤矿突水事故造成的危害。以古北煤矿为例，对矿区新生界松散含水层、二叠系砂岩裂隙含水层和石炭系太原组灰岩岩溶裂隙含水层水样的水质结果进行了测试，并选取K⁺、Na⁺、Ca²⁺、Mg²⁺、Cl⁻、SO₄²⁻、HCO₃⁻、TDS（总溶解固体）和pH作为主要指标，通过水化学成分分析、主要离子含量分析、Piper三线图分析和相关性分析研究含水层的水化学特征。随机选取35组水样导入SPSS软件进行因子分析（FA），并精简为三个主要因子作为人工神经网络模型的输入变量。基于MATLAB平台编写粒子群优化（PSO）代码，改进自调整权重和加速因子，用于优化反向传播（BP）神经网络的初始权重和阈值。训练样本与预测样本按8:2的比例进行学习，并将识别结果与传统BP神经网络模型进行比较。结果表明，古北煤矿地下水呈现水质垂向分带格局，化学成分以阳离子K⁺、Na⁺和阴离子Cl⁻为主。与聚类分析和FA-BP神经网络模型相比，FA-PSO-BP神经网络模型在水源判别方面具有更高的准确率。FA-PSO-BP神经网络模型在矿井突水水源识别问题上值得进一步应用。
