GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment. Lin, Y., Whitehouse, P. L., Valentine, A. P., & Woodroffe, S. A. Geophysical Research Letters, 50(18):e2023GL103672, 2023. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023GL103672![GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 \textless0.5 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.
@article{lin_georgia_2023,
title = {{GEORGIA}: {A} {Graph} {Neural} {Network} {Based} {EmulatOR} for {Glacial} {Isostatic} {Adjustment}},
volume = {50},
copyright = {© 2023. The Authors. Geophysical Research Letters published by Wiley Periodicals LLC on behalf of American Geophysical Union.},
issn = {1944-8007},
shorttitle = {{GEORGIA}},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2023GL103672},
doi = {10.1029/2023GL103672},
abstract = {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 {\textless}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.},
language = {en},
number = {18},
urldate = {2024-01-29},
journal = {Geophysical Research Letters},
author = {Lin, Yucheng and Whitehouse, Pippa L. and Valentine, Andrew P. and Woodroffe, Sarah A.},
year = {2023},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023GL103672},
keywords = {glacial isostatic adjustment, machine learning, sea-level change, statistical emulator},
pages = {e2023GL103672},
}
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