Russian Journal of Earth Sciences Russian Journal of Earth Sciences Russian Journal of Earth Sciences 1681-1208 90448 10.2205/2025es001018 umnein ОРИГИНАЛЬНЫЕ СТАТЬИ ORIGINAL ARTICLES ОРИГИНАЛЬНЫЕ СТАТЬИ Machine Learning for GNSS Time Series Analysis in the Time Domain Machine Learning for GNSS Time Series Analysis in the Time Domain https://orcid.org/0000-0001-8310-5112 Габсатаров Юрий Владимирович Gabsatarov Yuriy Vladimirovich gabsatarov.yv@ocean.ru

кандидат физико-математических наук;

candidate of physical and mathematical sciences;

 https://orcid.org/0000-0002-7301-7183 Владимирова Ирина Сергеевна Vladimirova Irina Sergeevna vladimirova.is@ocean.ru

кандидат физико-математических наук;

candidate of physical and mathematical sciences;

 Институт океанологии им. П.П. Ширшова РАН P.P.Shirshov Institute of Oceanology of the Russian Academy of Science Институт океанологии им. П.П. Ширшова РАН Москва Россия Shirshov Institute of Oceanology, Russian Academy of Sciences Moscow Russian Federation 30 12 2025 30 12 2025 25 6 ES6022 13 11 2024 29 04 2025 https://rjes.ru/en/nauka/article/90448/view

The paper presents the results of developing a method for analyzing time series of GNSS measurements based on a machine learning approach. The constructed algorithm was tested on GNSS data from the vicinity of sources of large earthquakes occurred in regions with different tectonic structures: the Japanese islands, Southern California, and the Peruvian-Chilean coast. It is shown that the proposed approach allows one to build an adequate, versatile, interpretable, statistically significant time series model using exclusively statistical data analysis methods, which will further allow one to create automated processing systems operating in a near-real-time mode.

The paper presents the results of developing a method for analyzing time series of GNSS measurements based on a machine learning approach. The constructed algorithm was tested on GNSS data from the vicinity of sources of large earthquakes occurred in regions with different tectonic structures: the Japanese islands, Southern California, and the Peruvian-Chilean coast. It is shown that the proposed approach allows one to build an adequate, versatile, interpretable, statistically significant time series model using exclusively statistical data analysis methods, which will further allow one to create automated processing systems operating in a near-real-time mode.

 satellite geodesy modern motions and deformations of Earth's surface machine learning time series analysis satellite geodesy modern motions and deformations of Earth's surface machine learning time series analysis The study was supported by a grant from the Russian Science Foundation No. 24-27-00176, https://rscf.ru/en/project/24-27-00176/. The study was supported by a grant from the Russian Science Foundation No. 24-27-00176, https://rscf.ru/en/project/24-27-00176/.

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