Leveraging the Empirical Wavelet Transform in Combination with Convolutional LSTM Neural Networks to Enhance the Accuracy of Polar Motion Prediction

作     者：Xu-Qiao Wang Lan Du Zhong-Kai Zhang Ze-Jun Liu Hao Xiang Xu-Qiao Wang;Lan Du;Zhong-Kai Zhang;Ze-Jun Liu;Hao Xiang

作者机构：College of Geospatial InformationInformation Engineering UniversityZhengzhou 450001China Henan Industrial Technology Academy of Spatio-Temporal Big DataZhengzhou 450046China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2024年第24卷第9期

页      面：214-224页

核心收录：

学科分类：07[理学] 070402[理学-天体测量与天体力学] 0704[理学-天文学] 

基　　金：supported by the National Natural Science Foundation of China(NSFC)under grant No.42304044 the Natural Science Foundation of Henan,China under grant No.222300420385 

主　　题：data analysis methods:miscellaneous astrometry reference systems Earth 

摘      要：High-precision polar motion prediction is of great significance for deep space exploration and satellite *** motion is affected by a variety of excitation factors,and nonlinear prediction methods are more suitable for polar motion *** order to explore the effect of deep learning in polar motion *** paper proposes a combined model based on empirical wavelet transform(EWT),Convolutional Neural Networks(CNN)and Long Short Term Memory(LSTM).By training and forecasting EOP 20C04 data,the effectiveness of the algorithm is verified,and the performance of two forecasting strategies in deep learning for polar motion prediction is *** results indicate that recursive multi-step prediction performs better than direct multi-step prediction for short-term forecasts within 15 days,while direct multi-step prediction is more suitable for medium and long-term *** the 365 days forecast,the mean absolute error of EWT-CNN-LSTM in the X direction and Y direction is 18.25 mas and 15.78 mas,respectively,which is 23.5% and 16.2% higher than the accuracy of Bulletin *** results show that the algorithm has a good effect in medium and long term polar motion prediction.