王莎 李坤王雅莉,等.上游式尾矿库浸润线观测值与库水位关联机制研究[J].河南理工大学学报（自然科学版）,2025,44(6):36-44.

WANG S, LI K, WANG Y L,et al.Correlation mechanism between phreatic line and reservoir water level observations in upstream-style tailings dams[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(6):36-44.

上游式尾矿库浸润线观测值与库水位关联机制研究

王莎1,2, 李坤1,2, 王雅莉1,2, 楚一帆1,2, 何斌斌1,2

1.矿冶科技集团有限公司，北京  100160；2.国家金属矿绿色开采国际联合研究中心，北京  100160

摘要: 目的 受库水位变幅影响，浸润线与库水位观测值呈现一定相关关系，但其相关作用机制尚需明确。  方法 采集典型上游式尾矿库浸润线和库水位的原型观测数据序列，建立相关统计回归模型，开展浸润线与库水位绝对值、水头、时效之间的关联关系研究，采用拟合优度判定回归效果；基于上述回归模型，采用灰色关联分析理论对比不同因子的重要性，获得不同因子与浸润线观测值的灰色关联系数，对不同因子的重要性进行排序，揭示浸润线的主要影响因素。  结果 统计模型中构建的各因子空间变化趋势与因变量存在较强的依赖关系。浸润线观测值与水头实测值因子、水头平方因子、水头3次方因子、前5~30日水头平均值和前5~30日平均水头上升速率因子有关，统计回归结果具有较高精度。  结论 不同位置浸润线观测值的统计回归模型效果不尽相同，但均与水头实测值和变化速率有关，并与时效体现一定的相关性，随着时间序列拉长，与时效的相关性逐渐减弱。

关键词:浸润线;库水位;统计回归模型;灰色关联分析;时效

doi:10.16186/j.cnki.1673-9787.2025030069

基金项目:国家重点研发计划项目（2023YFC3012200）；矿冶科技集团有限公司青年基金资助项目（04-2532）；矿冶科技集团探索基金资助项目（02-2271）

收稿日期:2025/03/30

修回日期:2025/06/12

出版日期:2025/10/14

Correlation mechanism between phreatic line and reservoir water level observations in upstream-style tailings dams

Wang Sha1,2, Li Kun1,2, Wang Yali1,2, Chu Yifan1,2, He Binbin1,2

1.Beijing General Research Institute of Mining & Metallurgy， Beijing  100160， China；2.National Center for International Joint Research on Green Metal Mining， Beijing  100160， China

Abstract: Objectives Due to fluctuations in reservoir water level， the phreatic line exhibits a certain correlation with water level observations. However， the underlying mechanism remains unclear.  Methods Prototype observation data of the phreatic line and reservoir water level from a typical upstream tailings dam were collected. Statistical regression models were established to analyze their relationships with absolute water level， hydraulic head， and time effect， with goodness-of-fit used to assess model performance. Based on these models， grey correlation analysis was applied to compare the importance of different factors， calculate correlation coefficients， and rank their significance， thereby identifying the key factors influencing the phreatic line. Results The spatial variation trends of the constructed factors showed strong dependence on the dependent variable. Phreatic line observations were significantly correlated with the measured hydraulic head， its square， cube， as well as the average head and head rise rate over the previous 5~30 days. The regression results demonstrated high accuracy. Conclusions Regression performance varied across monitoring positions， but consistently showed correlations with hydraulic head and its rate of change， along with some dependence on time effect. This correlation with time weakened as the observation period lengthened.

Key words: phreatic line; reservoir water level; statistical regression model; grey correlation analysis; time effect