Estimation of Earth Rotation Parameters and Prediction of Polar Motion Using Hybrid CNN–LSTM Model

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-01, Vol.15 (2), p.427
• Main Authors: Yu, Kehao, Yang, Kai, Shen, Tonghui, Li, Lihua, Shi, Haowei, Song, Xu
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Chandler motion; Earth movements; Earth rotation; ERP; Event-related potentials; Experiments; fast Fourier transform; Fast Fourier transformations; Feature extraction; Fourier transforms; Frequency variation; hybrid CNN–LSTM model; Interferometry; Long short-term memory; Mathematical models; Neural networks; Ordinary differential equations; Parameters; polar motion; Polar wandering (geology); Prediction models; Software; Time series; Universal time; Very long base interferometry; VLBI; Wavelet transforms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15020427

The Earth rotation parameters (ERPs), including polar motion (PMX and PMY) and universal time (UT1-UTC), play a central role in functions such as monitoring the Earth’s rotation and high-precision navigation and positioning. Variations in ERPs reflect not only the overall state of movement of the Earth, but also the interactions among the atmosphere, ocean, and land on the spatial and temporal scales. In this paper, we estimated ERP series based on very long baseline interferometry (VLBI) observations between 2011–2020. The results show that the average root mean square errors (RMSEs) are 0.187 mas for PMX, 0.205 mas for PMY, and 0.022 ms for UT1-UTC. Furthermore, to explore the high-frequency variations in more detail, we analyzed the polar motion time series spectrum based on fast Fourier transform (FFT), and our findings show that the Chandler motion was approximately 426 days and that the annual motion was about 360 days. In addition, the results also validate the presence of a weaker retrograde oscillation with an amplitude of about 3.5 mas. This paper proposes a hybrid prediction model that combines convolutional neural network (CNN) and long short-term memory (LSTM) neural network: the CNN–LSTM model. The advantages can be attributed to the CNN’s ability to extract and optimize features related to polar motion series, and the LSTM’s ability to make medium- to long-term predictions based on historical time series. Compared with Bulletin A, the prediction accuracies of PMX and PMY are improved by 42% and 13%, respectively. Notably, the hybrid CNN–LSTM model can effectively improve the accuracy of medium- and long-term polar motion prediction.