Model discrepancy of Earth polar motion using topological data analysis and convolutional neural network analysis

Dongjin Lee, Christopher Bresten, Kookhyoun Youm, Ki-Weon Seo and Jae-Hun Jung
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Dongjin Lee: Department of Mathematics, Ajou University, Suwon, Korea†Department of AI and Data Science, Ajou University, Suwon, Korea
Christopher Bresten: #x2020;Department of AI and Data Science, Ajou University, Suwon, Korea
Kookhyoun Youm: #x2021;Earth Science Education, Seoul National University, Seoul, Korea
Ki-Weon Seo: #x2021;Earth Science Education, Seoul National University, Seoul, Korea
Jae-Hun Jung: #x2020;Department of AI and Data Science, Ajou University, Suwon, Korea§Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, 14260-2900, USA

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 08, 1-19

Abstract: An accurate analysis of the polar motion variation is essential to understand the global change of the environment and predict useful information about short-term and long-term change in climate. Observation of polar motion excitation using multiple measurements including Very-Long-Baseline-Interferometry (VLBI) provides highly accurate measurement of polar motion variation. The observed polar motion excitation has been modeled with multiple geophysical models, but the discrepancies between observations and models still exist. In this paper, we propose two approaches for detecting the discrepancy of the polar motion excitation: topological data analysis (TDA) and convolutional neural network (CNN) analysis. Our methods clearly show that the observed polar motion has a different topological structure from the model data, and there are time periods that the model fails to represent the polar motion. Numerical results indicate that the proposed methods show promise for applications to polar motion signal analysis.

Keywords: Polar motion variations; time-series analysis; topological data analysis; convolutional neural networks (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S012918312050117X

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