Variational inference of ice shelf rheology with physics-informed machine learning
Abstract Floating ice shelves that fringe the coast of Antarctica resist the flow of grounded ice into the ocean. One of the key factors governing the amount of flow resistance an ice shelf provides is the rigidity (related to viscosity) of the ice that constitutes it. Ice rigidity is highly heterog...
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crcambridgeupr:10.1017/jog.2023.8 2024-06-23T07:46:09+00:00 Variational inference of ice shelf rheology with physics-informed machine learning Riel, Bryan Minchew, Brent 2023 http://dx.doi.org/10.1017/jog.2023.8 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143023000084 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Journal of Glaciology volume 69, issue 277, page 1167-1186 ISSN 0022-1430 1727-5652 journal-article 2023 crcambridgeupr https://doi.org/10.1017/jog.2023.8 2024-06-12T04:04:31Z Abstract Floating ice shelves that fringe the coast of Antarctica resist the flow of grounded ice into the ocean. One of the key factors governing the amount of flow resistance an ice shelf provides is the rigidity (related to viscosity) of the ice that constitutes it. Ice rigidity is highly heterogeneous and must be calibrated from spatially continuous surface observations assimilated into an ice-flow model. Realistic uncertainties in calibrated rigidity values are needed to quantify uncertainties in ice sheet and sea-level forecasts. Here, we present a physics-informed machine learning framework for inferring the full probability distribution of rigidity values for a given ice shelf, conditioned on ice surface velocity and thickness fields derived from remote-sensing data. We employ variational inference to jointly train neural networks and a variational Gaussian Process to reconstruct surface observations, rigidity values and uncertainties. Applying the framework to synthetic and large ice shelves in Antarctica demonstrates that rigidity is well-constrained where ice deformation is measurable within the noise level of the observations. Further reduction in uncertainties can be achieved by complementing variational inference with conventional inversion methods. Our results demonstrate a path forward for continuously updated calibrations of ice flow parameters from remote-sensing observations. Article in Journal/Newspaper Antarc* Antarctica Ice Sheet Ice Shelf Ice Shelves Journal of Glaciology Cambridge University Press Journal of Glaciology 1 20 |
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Open Polar |
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Cambridge University Press |
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language |
English |
description |
Abstract Floating ice shelves that fringe the coast of Antarctica resist the flow of grounded ice into the ocean. One of the key factors governing the amount of flow resistance an ice shelf provides is the rigidity (related to viscosity) of the ice that constitutes it. Ice rigidity is highly heterogeneous and must be calibrated from spatially continuous surface observations assimilated into an ice-flow model. Realistic uncertainties in calibrated rigidity values are needed to quantify uncertainties in ice sheet and sea-level forecasts. Here, we present a physics-informed machine learning framework for inferring the full probability distribution of rigidity values for a given ice shelf, conditioned on ice surface velocity and thickness fields derived from remote-sensing data. We employ variational inference to jointly train neural networks and a variational Gaussian Process to reconstruct surface observations, rigidity values and uncertainties. Applying the framework to synthetic and large ice shelves in Antarctica demonstrates that rigidity is well-constrained where ice deformation is measurable within the noise level of the observations. Further reduction in uncertainties can be achieved by complementing variational inference with conventional inversion methods. Our results demonstrate a path forward for continuously updated calibrations of ice flow parameters from remote-sensing observations. |
format |
Article in Journal/Newspaper |
author |
Riel, Bryan Minchew, Brent |
spellingShingle |
Riel, Bryan Minchew, Brent Variational inference of ice shelf rheology with physics-informed machine learning |
author_facet |
Riel, Bryan Minchew, Brent |
author_sort |
Riel, Bryan |
title |
Variational inference of ice shelf rheology with physics-informed machine learning |
title_short |
Variational inference of ice shelf rheology with physics-informed machine learning |
title_full |
Variational inference of ice shelf rheology with physics-informed machine learning |
title_fullStr |
Variational inference of ice shelf rheology with physics-informed machine learning |
title_full_unstemmed |
Variational inference of ice shelf rheology with physics-informed machine learning |
title_sort |
variational inference of ice shelf rheology with physics-informed machine learning |
publisher |
Cambridge University Press (CUP) |
publishDate |
2023 |
url |
http://dx.doi.org/10.1017/jog.2023.8 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143023000084 |
genre |
Antarc* Antarctica Ice Sheet Ice Shelf Ice Shelves Journal of Glaciology |
genre_facet |
Antarc* Antarctica Ice Sheet Ice Shelf Ice Shelves Journal of Glaciology |
op_source |
Journal of Glaciology volume 69, issue 277, page 1167-1186 ISSN 0022-1430 1727-5652 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/jog.2023.8 |
container_title |
Journal of Glaciology |
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1 |
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20 |
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1802644270331461632 |