@article{SUN2025, 
author = {Xiwen SUN and Xiaoxing HE and Tieding LU and Haicheng WANG and Yuntao ZHANG and Hongkang CHEN},
title = {Mix variational mode decomposition long short-term memory for predicting of reservoir surface displacement and deformation},
year = {2025},
journal = {Journal of National University of Defense Technology},
volume = {47},
number = {3},
pages = {151-161},
keywords = {artificial neural network, variational mode decomposition, long short-term memory network, deformation prediction},
url = {https://www.sciopen.com/article/10.11887/j.cn.202503016},
doi = {10.11887/j.cn.202503016},
abstract = {In order to improve the prediction accuracy of the displacement and deformation of reservoir, the displacement and deformation of non-linear and non-stationary reservoir was predicted by changing the decomposition method of VMD (variational mode decomposition) and integrating VMD and long short-term memory. A MVMDLSTM (mixed variational mode decomposition long short-term memory) model prediction method was proposed. The reliability of the new method was verified with multi-source datasets for different single prediction models and combined models. The experimental results show that the MVMDLSTM model can effectively attenuate the bias of the single prediction model and the empirical mode decomposition combination model estimation, and the prediction accuracy of the MVMDLSTM model is better, which provides an effective data decision-making for the stable monitoring of the prediction and warning of the reservoir′s slow sliding and creeping and other small deformations.}
}