韩忠, 王晓丽, 施龙青.PCA-BP神经网络在矿山岩溶突水水源判别中的应用研究[J].河南理工大学学报（自然科学版）,2023,42(1):46-53.

HAN Z, WANG X L, SHI L Q.Study on application of PCA-BP neural network in discrimination of karst water inrush source in mine[J].Journal of Henan Polytechnic University(Natural Science) ,2023,42(1):46-53.

PCA-BP神经网络在矿山岩溶突水水源判别中的应用研究

韩忠1, 王晓丽2, 施龙青2

1.山东省第六地质矿产勘查院，山东 威海 264209；2.山东科技大学 地球科学与工程学院，山东 青岛  266590

摘要:肥城煤田奥陶系灰岩水和徐家庄灰岩水水质十分相近，因此，导致该煤田矿井突水水源难以判别，为了解决这一问题，选取突水水源中F，Br，I，Rn和H₃BO₃等微量元素以及化合物质量浓度作为判别指标，利用SPSS软件进行主成分分析，并将所得主成分代入MATLAB软件，建立PCA-BP神经网络判别模型，对比PCA-BP神经网络模型与BP神经网络模型、系统聚类分析判别模型、Fisher判别分析模型的收敛速度和输出精度。结果表明：PCA-BP神经网络模型判别准确率为100%，具有输出结果精度高、误差小、收敛速度快、训练次数少等优点。该模型对于识别水质相近的灰岩突水水源具有一定应用价值。

关键词:肥城煤田;突水水源;PCA-BP神经网络;奥陶系灰岩;徐家庄灰岩;判别精度

doi:10.16186/j.cnki.1673-9787.2020070099

基金项目:国家自然科学基金资助项目（51804184）；山东省自然科学基金资助项目（ZR2020KE023）

收稿日期:2020/07/09

修回日期:2021/12/27

出版日期:2023/01/25

Study on application of PCA-BP neural network in discrimination of karst water inrush source in mine

HAN Zhong1, WANG Xiaoli2, SHI Longqing2

1.No.6 Institution of Geology and Mineral Resources of Shandong Province，Weihai  264209，Shandong，China；2.College of Geosciences and Engineering，Shandong University of Science and Technology，Qingdao  266590，Shandong，China

Abstract:The water quality of Ordovician limestone and Xujiazhuang limestone in Feicheng Coalfield is very similar，so that it is difficult to discriminate the source of water inrush.In order to solve this problem，five trace elements including F，Br，I，H₃BO₃ and Rn were selected as discriminant indexes.SPSS software was used to model principal component analysis，the PCA-BP neural network model was established by substituting the principal components into MATLAB software.In terms of convergence process and output accuracy，PCA-BP neural network model was compared with BP neural network model，system clustering analysis discriminant model and Fisher discriminant analysis model.The results showed that the accuracy of PCA-BP neural network model was 100%，it hasd the advantages of the highest output accuracy，the smallest error，the fast convergence speed and the few iterations.Therefore，the model had a certain application value for discriminating similar limestone water inrush sources.

Key words:Feicheng coalfield;water inrush source;PCA-BP neural network;Ordovician limestone;Xujiazhuang limestone;discriminant accuracy

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