Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica
International audience A method to infer the bed elevation from glaciers surface measurements (eleva-tion, velocity) and sparse in-situ thickness values is developed and assessed. This inversion method relies on: a statistical model (Deep Neural Network) based on the in-situ thickness measurements,...
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Online Access: | https://hal.science/hal-01926620 https://hal.science/hal-01926620v4/document https://hal.science/hal-01926620v4/file/MonnierZhu-ComputSc21.pdf https://doi.org/10.1007/s10596-021-10070-1 |
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ftunivparis:oai:HAL:hal-01926620v4 2024-06-02T07:56:55+00:00 Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica Monnier, Jérôme Zhu, Jiamin Institut de Mathématiques de Toulouse UMR5219 (IMT) Université Toulouse Capitole (UT Capitole) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse) Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J) Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS) Institut National des Sciences Appliquées - Toulouse (INSA Toulouse) Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT) Laboratoire Jacques-Louis Lions (LJLL) Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS) 2021-10 https://hal.science/hal-01926620 https://hal.science/hal-01926620v4/document https://hal.science/hal-01926620v4/file/MonnierZhu-ComputSc21.pdf https://doi.org/10.1007/s10596-021-10070-1 en eng HAL CCSD Springer Verlag info:eu-repo/semantics/altIdentifier/doi/10.1007/s10596-021-10070-1 hal-01926620 https://hal.science/hal-01926620 https://hal.science/hal-01926620v4/document https://hal.science/hal-01926620v4/file/MonnierZhu-ComputSc21.pdf doi:10.1007/s10596-021-10070-1 info:eu-repo/semantics/OpenAccess ISSN: 1420-0597 EISSN: 1573-1499 Computational Geosciences https://hal.science/hal-01926620 Computational Geosciences, 2021, 25 (5), pp.1793-1819. ⟨10.1007/s10596-021-10070-1⟩ Data assimilation reduced flow model surface data bed inference Antarctica deep learning inference topography glaciers [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS] [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology info:eu-repo/semantics/article Journal articles 2021 ftunivparis https://doi.org/10.1007/s10596-021-10070-1 2024-05-07T02:53:50Z International audience A method to infer the bed elevation from glaciers surface measurements (eleva-tion, velocity) and sparse in-situ thickness values is developed and assessed. This inversion method relies on: a statistical model (Deep Neural Network) based on the in-situ thickness measurements, the dedicated RU-SIA flow model (RU for Reduced Uncertainty) natively integrating the surface measurements (altimetry, InSAR) and advanced Variational Data Assimilation processes. The RU-SIA model takes into account basal slipperiness and non uniform vertical profiles (including thermal gradients) via an unique dimensionless parameter. The inversion method is robust; it may be applied to very poorly covered and uncovered areas during airborne campaigns as soon as flows are moderately sheared. Numerical inversions are performed for some large East Antarctica Ice Sheet areas presenting surface velocities ranging from ∼ 5 to 80 m/y. Estimations are provided in uncovered areas during airborne campaigns hence presenting up to now highly uncertain bed elevation values. The estimations are valid for wave lengths greater than ∼ 10 ¯ h due to the considered shallow flow assumption, with a resolution at ∼ ¯ h (¯ h a characteristic thickness value). Detailed analysis and comparisons with the bed topography BedMap2 are presented. Article in Journal/Newspaper Antarc* Antarctica East Antarctica Ice Sheet Université de Paris: Portail HAL East Antarctica Computational Geosciences 25 5 1793 1819 |
institution |
Open Polar |
collection |
Université de Paris: Portail HAL |
op_collection_id |
ftunivparis |
language |
English |
topic |
Data assimilation reduced flow model surface data bed inference Antarctica deep learning inference topography glaciers [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS] [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology |
spellingShingle |
Data assimilation reduced flow model surface data bed inference Antarctica deep learning inference topography glaciers [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS] [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology Monnier, Jérôme Zhu, Jiamin Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica |
topic_facet |
Data assimilation reduced flow model surface data bed inference Antarctica deep learning inference topography glaciers [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS] [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology |
description |
International audience A method to infer the bed elevation from glaciers surface measurements (eleva-tion, velocity) and sparse in-situ thickness values is developed and assessed. This inversion method relies on: a statistical model (Deep Neural Network) based on the in-situ thickness measurements, the dedicated RU-SIA flow model (RU for Reduced Uncertainty) natively integrating the surface measurements (altimetry, InSAR) and advanced Variational Data Assimilation processes. The RU-SIA model takes into account basal slipperiness and non uniform vertical profiles (including thermal gradients) via an unique dimensionless parameter. The inversion method is robust; it may be applied to very poorly covered and uncovered areas during airborne campaigns as soon as flows are moderately sheared. Numerical inversions are performed for some large East Antarctica Ice Sheet areas presenting surface velocities ranging from ∼ 5 to 80 m/y. Estimations are provided in uncovered areas during airborne campaigns hence presenting up to now highly uncertain bed elevation values. The estimations are valid for wave lengths greater than ∼ 10 ¯ h due to the considered shallow flow assumption, with a resolution at ∼ ¯ h (¯ h a characteristic thickness value). Detailed analysis and comparisons with the bed topography BedMap2 are presented. |
author2 |
Institut de Mathématiques de Toulouse UMR5219 (IMT) Université Toulouse Capitole (UT Capitole) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse) Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J) Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS) Institut National des Sciences Appliquées - Toulouse (INSA Toulouse) Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT) Laboratoire Jacques-Louis Lions (LJLL) Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Monnier, Jérôme Zhu, Jiamin |
author_facet |
Monnier, Jérôme Zhu, Jiamin |
author_sort |
Monnier, Jérôme |
title |
Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica |
title_short |
Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica |
title_full |
Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica |
title_fullStr |
Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica |
title_full_unstemmed |
Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica |
title_sort |
physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. application to east antarctica |
publisher |
HAL CCSD |
publishDate |
2021 |
url |
https://hal.science/hal-01926620 https://hal.science/hal-01926620v4/document https://hal.science/hal-01926620v4/file/MonnierZhu-ComputSc21.pdf https://doi.org/10.1007/s10596-021-10070-1 |
geographic |
East Antarctica |
geographic_facet |
East Antarctica |
genre |
Antarc* Antarctica East Antarctica Ice Sheet |
genre_facet |
Antarc* Antarctica East Antarctica Ice Sheet |
op_source |
ISSN: 1420-0597 EISSN: 1573-1499 Computational Geosciences https://hal.science/hal-01926620 Computational Geosciences, 2021, 25 (5), pp.1793-1819. ⟨10.1007/s10596-021-10070-1⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10596-021-10070-1 hal-01926620 https://hal.science/hal-01926620 https://hal.science/hal-01926620v4/document https://hal.science/hal-01926620v4/file/MonnierZhu-ComputSc21.pdf doi:10.1007/s10596-021-10070-1 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1007/s10596-021-10070-1 |
container_title |
Computational Geosciences |
container_volume |
25 |
container_issue |
5 |
container_start_page |
1793 |
op_container_end_page |
1819 |
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