Reconstruction of geodetic time series with missing data and time-varying seasonal signals using Gaussian process for machine learning

Xu, Keke; Hu, Shaobin; Jin, Shuanggen; Li, Jun; Zheng, Wei; Wang, Jian; Zhu, Yongzhen; Li, Kezhao; Ren, Ankang; Liu, Yifu

doi:10.1007/s10291-024-01616-8

GPS Solut

2024

DOI: 10.1007/s10291-024-01616-8

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Reconstruction of geodetic time series with missing data and time-varying seasonal signals using Gaussian process for machine learning

Keke Xu,

Shaobin Hu,

Shuanggen Jin

et al.

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Cited by 3 publications

References 36 publications

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gCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

Zhang,

Li,

Zhao

et al. 2025

Advances in Space Research

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gCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

Zhang,

Li,

Zhao

et al. 2025

Advances in Space Research

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An improved Gaussian process for filling the missing data in GNSS position time series considering the influence of adjacent stations

Qiu,

Wang,

Zhang

et al. 2024

Sci Rep

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Monitoring terrestrial water storage changes using GNSS vertical coordinate time series in Amazon River basin

Liu,

Xu,

Guo

et al. 2024

Sci Rep

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Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

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Reconstruction of geodetic time series with missing data and time-varying seasonal signals using Gaussian process for machine learning

Cited by 3 publications

References 36 publications

gCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

gCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

An improved Gaussian process for filling the missing data in GNSS position time series considering the influence of adjacent stations

Monitoring terrestrial water storage changes using GNSS vertical coordinate time series in Amazon River basin

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