煤矿涌水水源快速判识方法在神东矿区的应用  

Application of rapid identification method for coal mine water inrush source in Shendong mining area

作　　者：陶志勇 TAO Zhiyong(China Energy Shendong Coal Group Co.,Ltd.,Yulin,Shaanxi 719315,China;Shendong Coal Branch of China Shenhua Energy Co.,Ltd.,Yulin,Shaanxi 719315,China)

机构地区：[1]国能神东煤炭集团有限责任公司,陕西省榆林市719315 [2]中国神华能源股份有限公司神东煤炭分公司,陕西省榆林市719315

出　　处：《中国煤炭》2025年第2期79-87,共9页China Coal

摘　　要：如何智能地进行涌水水源识别是矿井采取对应水害防治措施的关键,为精准识别神东矿区涌水水源,提出一种基于核主成分分析(KPCA)及沙猫群优化算法(SCSO)结合支持向量机(SVM)的矿井涌水水源识别模型(KPCA SCSO SVM),通过数据降维及高效、自适应性的优化算法进一步提高涌水水源识别的效率及准确率。根据神东矿区水文地质条件选取了56组水样样本,将该模型应用于神东矿区的涌水水源识别工程实例中,并与水化学特征分析方法及其他优化算法模型进行对比实验。结果表明,所提出的KPCA SCSO SVM涌水水源识别模型获得了最佳性能,在神东矿区的水源识别验证数据集上,全部正确识别出了水样样本的来源,为神东矿区水害防治措施提供重要支撑。How to intelligently identify water inflow source is the key for coal mine to take corresponding water disaster prevention measures.In order to accurately identify water inrush source in Shendong mining area,a mine water inrush source identification model(KPCA SCSO SVM)based on core principal component analysis(KPCA),sand cat group optimization algorithm(SCSO)and support vector machine(SVM)was proposed.The efficiency and accuracy of water inrush source identification were further improved by data dimensionality reduction and efficient-adaptive optimization algorithm.According to the hydrogeological conditions of Shendong mining area,56 groups of water samples were selected,and the model was applied to the water inflow source identification project of Shendong mining area,and the comparison test was carried out with the hydrochemical characteristic analysis method and other optimization algorithm models.The results showed that the proposed KPCA SCSO SVM model had achieved the best performance of water inrush source identification,and all the sources of water samples were correctly identified on the verification data set of water source identification in Shendong mining area,which provided basis and reference for the water disaster prevention measures in Shendong mining area.

关 键 词：矿井涌水 水源智能识别 水化学特征 沙猫群优化算法 支持向量机 

分 类 号：TD745[矿业工程—矿井通风与安全]