桑向阳, 林云, 刘保民,等.基于水位、水温、突水量和水质的充水水源识别神经网络模型[J].河南理工大学学报（自然科学版）,2024,43(5):36-42.

SANG X Y, LIN Y, LIU B M,et al.Neural network model of water filling source identification based on water level，water temperature，water intrusion，and water quality[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(5):36-42.

基于水位、水温、突水量和水质的充水水源识别神经网络模型

桑向阳1, 林云2, 刘保民2, 潘国营2

1.中国平煤神马集团 地质测量处，河南 平顶山  467000;2.河南理工大学 资源环境学院，河南 焦作  454000

摘要:华北石炭-二叠系岩溶型煤田主采煤层底板的太原组薄层灰岩岩溶水和奥陶系或寒武系厚层灰岩岩溶水的水化学特征存在着天然相似性，单纯以若干项水化学指标辨识这些水源存在着误判甚至错判的风险。 目的 为解决寒武系灰岩水、太原组下段灰岩水和部分太原组上段灰岩水的水质指标相似而难以完全正确识别的问题，  方法 提出并构建基于水位、水温、突水量和水质识别充水水源的神经网络模型。以平顶山矿区充水水源识别为例，以阴阳离子毫克当量百分比［γ（Na）% ，γ（Ca+Mg）% ，γ（HCO3-）% ，γ（SO₄+Cl）%］、钠钙比、碱硬比、ρ（CO32-）、ρ（SO42-）、TDS、ρ（Na+K）、水位、水位动态变化、水温、突水量、衰减天数共15项指标作为识别因子，构建结构为15-10-6的神经网络模型。  结果 结果表明，所有训练样本对自身水源的拟合均值均超过0.98，比单纯以水质指标为识别因子的建模方法识别正确率高，能够有效消除因水质指标相似但水源不同而出现的误判或错判情况。  结论 建模方法已经嵌入平顶山矿区识别充水水源计算机软件和手机APP软件中，经过检验，识别正确率达到91.3%。

关键词:煤矿水源识别;水位;水温;突水量;水质;神经网络

doi:10.16186/j.cnki.1673-9787.2022070002

基金项目:国家自然科学基金资助项目（42271041）；河南省高等学校青年骨干教师培养计划项目（2021GGJS055）

收稿日期:2022/07/01

修回日期:2022/11/28

出版日期:2024/07/31

Neural network model of water filling source identification based on water level，water temperature，water intrusion，and water quality

SANG Xiangyang1, LIN Yun2, LIU Baomin2, PAN Guoying2

1.Geological Survey Department，China Pingmei Shenma Group，Pingdingshan  467000，Henan，China;2.School of Resources and Environment，Henan Polytechnic University，Jiaozuo  454000，Henan，China

Abstract:The water chemical characteristics of thin limestone karst water in the Taiyuan Formation and thick Ordovician or Cambrian thick limestone karst water in the main coal seam floor of Carboniferous-Permian karst coal field in North China are naturally similar.This similarity poses a risk of misjudgment or even miscalculation when relying solely on certain hydrochemical indexes.  Objectives The water quality indexes of limestone water，L2 limestone water，and some L7 limestone water are similar，making accurate identification challenging.To address this issue，  Methods a neural network model for identifying water sources based on water level，temperature，quantity，and quality was proposed.Taking the filling water source identification of the Pingdingshan mining area as an example，a 15-10-6 neural network model was constructed with 15 indexes as identification factors，including the anion and cation percentages in milligram equivalents，the ratio of sodium to calcium，the ratio of alkali to hardness，ρ（CO32-）,ρ（SO42-）,TDS,ρ（Na+K），water level，dynamic change，water temperature，water intrusion，and attenuation days. Results The experimental results showed that the mean value of all training samples’ fitting to their own water sources exceeded 0.98，which significantly improved the recognition accuracy compared with the modeling method that simply took water quality index as the recognition factor，and could completely and effectively eliminate the misjudgment caused by similar water quality indexes but different water sources.  Conclusions The proposed modeling method had been incorporated into the computer software and mobile app software for identifying water sources in the Pingdingshan mining area.After testing，the recognition accuracy reached 91.3%.

Key words:coal mine water source identification;water level;water temperature;water intrusion;water quality;neural network