Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica

Abstract Reliable projections of sea‐level rise depend on accurate representations of how fast‐flowing glaciers slip along their beds. The mechanics of slip are often parameterized as a constitutive relation (or “sliding law”) whose proper form remains uncertain. Here, we present a novel deep learni...

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Autores principales:	B. Riel, B. Minchew, T. Bischoff
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/6cf18a304d24415f8fc32bc86dd9c0a8
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id	oai:doaj.org-article:6cf18a304d24415f8fc32bc86dd9c0a8
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spelling	oai:doaj.org-article:6cf18a304d24415f8fc32bc86dd9c0a82021-11-30T08:40:32ZData‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica1942-246610.1029/2021MS002621https://doaj.org/article/6cf18a304d24415f8fc32bc86dd9c0a82021-11-01T00:00:00Zhttps://doi.org/10.1029/2021MS002621https://doaj.org/toc/1942-2466Abstract Reliable projections of sea‐level rise depend on accurate representations of how fast‐flowing glaciers slip along their beds. The mechanics of slip are often parameterized as a constitutive relation (or “sliding law”) whose proper form remains uncertain. Here, we present a novel deep learning‐based framework for learning the time evolution of drag at glacier beds from time‐dependent ice velocity and elevation observations. We use a feedforward neural network, informed by the governing equations of ice flow, to infer spatially and temporally varying basal drag and associated uncertainties from data. We test the framework on 1D and 2D ice flow simulation outputs and demonstrate the recovery of the underlying basal mechanics under various levels of observational and modeling uncertainties. We apply this framework to time‐dependent velocity data for Rutford Ice Stream, Antarctica, and present evidence that ocean‐tide‐driven changes in subglacial water pressure drive changes in ice flow over the tidal cycle.B. RielB. MinchewT. BischoffAmerican Geophysical Union (AGU)articlePhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 13, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	Physical geography GB3-5030 Oceanography GC1-1581 B. Riel B. Minchew T. Bischoff Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica
description	Abstract Reliable projections of sea‐level rise depend on accurate representations of how fast‐flowing glaciers slip along their beds. The mechanics of slip are often parameterized as a constitutive relation (or “sliding law”) whose proper form remains uncertain. Here, we present a novel deep learning‐based framework for learning the time evolution of drag at glacier beds from time‐dependent ice velocity and elevation observations. We use a feedforward neural network, informed by the governing equations of ice flow, to infer spatially and temporally varying basal drag and associated uncertainties from data. We test the framework on 1D and 2D ice flow simulation outputs and demonstrate the recovery of the underlying basal mechanics under various levels of observational and modeling uncertainties. We apply this framework to time‐dependent velocity data for Rutford Ice Stream, Antarctica, and present evidence that ocean‐tide‐driven changes in subglacial water pressure drive changes in ice flow over the tidal cycle.
format	article
author	B. Riel B. Minchew T. Bischoff
author_facet	B. Riel B. Minchew T. Bischoff
author_sort	B. Riel
title	Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica
title_short	Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica
title_full	Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica
title_fullStr	Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica
title_full_unstemmed	Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica
title_sort	data‐driven inference of the mechanics of slip along glacier beds using physics‐informed neural networks: case study on rutford ice stream, antarctica
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/6cf18a304d24415f8fc32bc86dd9c0a8
work_keys_str_mv	AT briel datadriveninferenceofthemechanicsofslipalongglacierbedsusingphysicsinformedneuralnetworkscasestudyonrutfordicestreamantarctica AT bminchew datadriveninferenceofthemechanicsofslipalongglacierbedsusingphysicsinformedneuralnetworkscasestudyonrutfordicestreamantarctica AT tbischoff datadriveninferenceofthemechanicsofslipalongglacierbedsusingphysicsinformedneuralnetworkscasestudyonrutfordicestreamantarctica
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Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica

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