Publications related to the GRACE Missions (no abstracts)

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Neural network-based framework for signal separation in spatio-temporal gravity data

Heller-Kaikov, Betty, Pail, Roland, and Werner, Martin, 2026. Neural network-based framework for signal separation in spatio-temporal gravity data. Computers and Geosciences, 207:106057, doi:10.1016/j.cageo.2025.106057.

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@ARTICLE{2026CG....20706057H,
       author = {{Heller-Kaikov}, Betty and {Pail}, Roland and {Werner}, Martin},
        title = "{Neural network-based framework for signal separation in spatio-temporal gravity data}",
      journal = {Computers and Geosciences},
     keywords = {Geospatial AI, Signal separation, Satellite gravity data, GRACE, Neural networks},
         year = 2026,
        month = feb,
       volume = {207},
          eid = {106057},
        pages = {106057},
     abstract = "{Global, temporal gravity data such as those provided by the Gravity
        Recovery and Climate Experiment (GRACE) and GRACE-Follow on
        (GRACE-FO) satellite missions contain signals from many mass
        redistribution processes on Earth. These include hydrological,
        atmospheric, oceanic, cryospheric and solid Earth-related
        processes. As the measured gravity changes represent the sum of
        all signals, an optimal exploitation of these data for
        scientific applications requires strategies for separating the
        individual contained signals. We provide a neural network
        algorithm using a multi-channel U-Net architecture that
        translates the sum of several signals to the individual
        contained components based on their typical
        space{\textendash}time patterns. The software contains
        strategies for transforming spatio-temporal gravity data
        depending on latitude, longitude, and time to 2-D ``image''
        training samples. The software also includes implementations of
        strategies for introducing additional knowledge about the
        physical behavior of the individual signals as constraints to
        the training. In a closed-loop simulation example, simulated
        gravity signals induced by processes in the atmosphere and
        oceans, hydrosphere, cryosphere and solid Earth are successfully
        separated at relative RMS prediction errors between 19 and 67\%.
        This shows that neural network-based methods can help solving
        geodetic tasks if the considered data is transformed into a
        suitable data format. To apply the framework to real
        observational data, we suggest training the network on
        representative, physical forward-modeled signals and
        subsequently applying the trained network to real data. The
        latter will additionally require external validation strategies.
        The software is freely available on GitHub under
        https://github.com/Betty-Heller/neural-gravity and is, in
        general, also applicable for signal separation in any other
        dataset depending on three variables.}",
          doi = {10.1016/j.cageo.2025.106057},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026CG....20706057H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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