煤矿安全2025，Vol.56Issue(9)：44-51,8.DOI:10.13347/j.cnki.mkaq.20250248
基于EEMD-LSTM模型网络的露天矿边坡GNSS监测数据多尺度解析
Multi-scale analysis of GNSS monitoring data of open-pit mine slopes based on EEMD-LSTM model network
摘要
Abstract
The EEMD-LSTM model is proposed to enhance the prediction accuracy and stability of GNSS monitoring data for open-pit mine slopes,addressing challenges in traditional methods such as mode mixing,noise interference,and insufficient capture of long-term dependent features in handling complex nonlinear time series data.This model integrates ensemble empirical mode de-composition(EEMD)and long short-term memory(LSTM)networks.Firstly,the EEMD algorithm adaptively decomposes raw monitoring signals into multiple intrinsic mode functions(IMFs),effectively separating noise and sudden anomaly information to re-solve the mode mixing issue inherent in traditional empirical mode decomposition(EMD).Secondly,the LSTM network extracts temporal features from decomposed IMFs and enhances long-term dependency modeling through its gate control mechanism.Lastly,an improved data isolation procedure(involving repeated decomposition and independent predictions)prevents information leakage,while multi-dimensional error evaluation metrics(MAE,MAPE,RMSE)validate model performance.Experimental validation util-ized GNSS monitoring data from an open-pit mine in Heilongjiang Province,processing 6 727 displacement datasets to predict 30-day deformation trends.Results demonstrated that EEMD successfully isolated high-frequency noise(IMF1,IMF2)and low-fre-quency trend components(IMF8,IMF9),significantly reducing anomaly-induced prediction interference.The model exhibited op-timal performance in 3D displacement prediction,achieving the highest precision in the z-direction(RMSE=0.017),though systemat-ic bias in the y-direction requires further optimization.The improved isolation process notablely reduced errors in 2D/3D predictions,confirming the model's resistance to information leakage.Correlation with slope stability calculations showed safety factor improve-ment to 1.21 after treatment,with monitoring point displacements stabilizing within 50 mm.By combining signal decomposition with deep learning,the EEMD-LSTM framework enables multi-scale analysis of complex time series features,providing a high-pre-cision dynamic prediction tool for slope disaster early warning systems in open-pit mining environments.关键词
EEMD-LSTM/边坡监测/GNSS/边坡稳定性分析/边坡治理/滑坡Key words
EEMD-LSTM/slope monitoring/GNSS/slope stability analysis/slope management/landslide分类
矿业与冶金引用本文复制引用
高晗,王可可,马可,王勇,张凯..基于EEMD-LSTM模型网络的露天矿边坡GNSS监测数据多尺度解析[J].煤矿安全,2025,56(9):44-51,8.基金项目
中煤科工集团沈阳设计研究院有限公司重大科技资助项目(NKJ016-2023) （NKJ016-2023）
新疆维吾尔自治区重大科技专项资助项目(2024A01002-4) （2024A01002-4）
