李建林, 贺奇, 王树威,等.相空间重构后矿井涌水量序列地质学含义及其应用研究[J].河南理工大学学报（自然科学版）,2024,43(5):43-52.

LI J L, HE Q, WANG S W,et al.Study on the geological meaning and application of mine water inflow series after phase space reconstruction[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(5):43-52.

相空间重构后矿井涌水量序列地质学含义及其应用研究

李建林1,2, 贺奇1, 王树威1, 王心义1,2, 王冲4, 薛杨1

1.河南理工大学 资源环境学院，河南 焦作  454000;2.煤炭安全生产与清洁高效利用省部共建协同创新中心，河南 焦作 454000;3.中化地质矿山总局 浙江地质勘查院，浙江 杭州  310002;4.天津华北地质勘查局 核工业二四七大队，天津  301800

摘要: 目的 为了确定相空间重构矿井涌水量序列的地质学含义并提高涌水量预测精度，  方法 以王行庄矿为例，在涌水量序列相空间重构后，对重构后相空间列向量与涌水量主控因素进行相关性分析，并在此基础上建立混沌理论与人工神经网络耦合（Chaos-ENN）的涌水量预测模型。  结果 结果表明：相空间的嵌入维数等于矿井涌水量主控因素个数；相空间的第1，2，4，5，6列向量分别与C2tL7-8含水层水位埋深、O2m+Є3ch含水层水位埋深、采空区面积、C2tL1-4含水层水位埋深、开拓长度具有较高的相关性，第3列与不易量化的其他综合因素有关；构建的Chaos-ENN涌水量预测模型在王兴庄矿的预测精度达到97.91%。  结论 涌水量序列重构后相空间的列向量具有明确的地质学含义。利用混沌理论可以量化涌水量预测模型中ENN输入层的个数及取值，所以仅需涌水量序列值就可以建立矿井涌水量预测的Chaos-ENN模型，该模型解决了涌水量预测中存在的主控因素难以确定和不易量化的难题，且预测精度高，具有较高的推广价值。

关键词:矿井水文系统;相空间重构;涌水量主控因素;混沌特征;Chaos-ENN预测模型

doi:10.16186/j.cnki.1673-9787.2023040006

基金项目:国家自然科学基金资助项目（41972254）；河南省高等学校重点科研项目（22A170009）

收稿日期:2023/04/06

修回日期:2023/05/16

出版日期:2024/07/31

Study on the geological meaning and application of mine water inflow series after phase space reconstruction

LI Jianlin1,2, HE Qi1, WANG Shuwei1, WANG Xinyi1,2, WANG Chong4, XUE Yang1

1.School of Resources and Environment，Henan Polytechnic University，Jiaozuo  454000，Henan，China;2.Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization，Jiaozuo  454000，Henan，China;3.Zhejiang Geological Prospecting Institute，China Chemical Geology and Mine Bureau，Hangzhou  310002，Zhejiang，China;4.The Nuclear Industry 247 Brigade，Tianjin North China Geological Exploration Bureau，Tianjin  301800，China

Abstract: Objectives To determine the geological meaning of mine water inflow series after phase space reconstruction and improve the accuracy of water inflow prediction， Methods taking Wangxingzhuang Mine as an example，the correlation between column vectors in the reconstructed phase space of the water inflow sequence and the main control factors of mine water inflow was analyzed.On this basis，a water inflow prediction model coupled with chaos theory and artificial neural network （Chaos-ENN） was established. Results The results showed that the embedding dimension of the phase space was equal to the number of main control factors of mine water inflow.The reconstructed phase space columns 1，2，4，5 and 6 were highly correlated with the depth of the C2tL7-8 aquifer burial，the depth of the O2m+Є3ch aquifer burial，the goaf area，the depth of the C2tL1-4 aquifer burial，and the development length，respectively.The column 3 was related to other comprehensive factors that were difficult to quantify.The prediction accuracy of the Chaos-ENN model at Wangxingzhuang Mine reacheded 97.91%. Conclusions The column vectors of the phase space after the reconstruction of the water inflow sequence had clear geological meaning.Chaos theory quantified the number and values of the ENN input layers in the water inflow prediction model，allowing the establishment of a Chaos-ENN model for mine water inflow prediction using only the sequence values of water inflow.This model addressed the challenges of determining and quantifying the main controlling factors in water inflow prediction.It achieved high prediction accuracy and demonstrated significant potential for broader application.

Key words:mine hydrological system;phase space reconstruction;main control factor of water inflow;chaotic characteristic;Chaos-ENN prediction model