Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-645-2025
https://doi.org/10.5194/tc-19-645-2025
Research article
 | 
07 Feb 2025
Research article |  | 07 Feb 2025

Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard

Viola Steidl, Jonathan Louis Bamber, and Xiao Xiang Zhu

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Cited articles

Anilkumar, R., Bharti, R., Chutia, D., and Aggarwal, S. P.: Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques, The Cryosphere, 17, 2811–2828, https://doi.org/10.5194/tc-17-2811-2023, 2023. a
Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., and Sauquet, E.: Deep learning applied to glacier evolution modelling, The Cryosphere, 14, 565–584, https://doi.org/10.5194/tc-14-565-2020, 2020. a, b
Bouchayer, C., Aiken, J. M., Thøgersen, K., Renard, F., and Schuler, T. V.: A Machine Learning Framework to Automate the Classification of Surge-Type Glaciers in Svalbard, J. Geophys. Res.-Earth, 127, e2022JF006597, https://doi.org/10.1029/2022JF006597, 2022. a
Cheng, G., Morlighem, M., and Francis, S.: Forward and Inverse Modeling of Ice Sheet Flow Using Physics-Informed Neural Networks: Application to Helheim Glacier, Greenland, Journal of Geophysical Research: Machine Learning and Computation, 1, e2024JH000169, https://doi.org/10.1029/2024JH000169, 2024. a, b
Copernicus: Copernicus DEM GLO-90, Copernicus [data set], https://doi.org/10.5270/ESA-c5d3d65, 2019. a
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Short summary
Glacier ice thickness is difficult to measure directly but is essential for glacier evolution modelling. In this work, we employ a novel approach combining physical knowledge and data-driven machine learning to estimate the ice thickness of multiple glaciers in Spitsbergen, Barentsøya, and Edgeøya in Svalbard. We identify challenges for the physics-aware machine learning model and opportunities for improving the accuracy and physical consistency that would also apply to other geophysical tasks.
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