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GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment

Lin, Yucheng; Whitehouse, Pippa L.; Valentine, Andrew P.; Woodroffe, Sarah A.


Yucheng Lin
PGR Student Doctor of Philosophy


Glacial isostatic adjustment (GIA) modeling is not only useful for understanding past relative sea-level change but also for projecting future sea-level change due to ongoing land deformation. However, GIA model predictions are subject to a range of uncertainties, most notably due to uncertainty in the input ice history. An effective way to reduce this uncertainty is to perform data-model comparisons over a large ensemble of possible ice histories, but this is often impossible due to computational limitations. Here we address this problem by building a deep-learning-based GIA emulator that can mimic the behavior of a physics-based GIA model while being computationally cheap to evaluate. Assuming a single 1-D Earth rheology, our emulator shows 0.54 m mean absolute error on 150 out-of-sample testing data with <0.5 s emulation time. Using this emulator, two illustrative applications related to the calculation of barystatic sea level are provided for use by the sea-level community.


Lin, Y., Whitehouse, P. L., Valentine, A. P., & Woodroffe, S. A. (2023). GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment. Geophysical Research Letters, 50(18),

Journal Article Type Article
Acceptance Date Aug 9, 2023
Online Publication Date Sep 16, 2023
Publication Date Sep 28, 2023
Deposit Date Nov 1, 2023
Journal Geophysical Research Letters
Print ISSN 0094-8276
Electronic ISSN 1944-8007
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 50
Issue 18
Keywords General Earth and Planetary Sciences; Geophysics
Public URL
Additional Information Received: 2023-03-13; Accepted: 2023-08-09; Published: 2023-09-16