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The Cryosphere An interactive open-access journal of the European Geosciences Union
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TC | Articles | Volume 14, issue 11
The Cryosphere, 14, 3687–3705, 2020
https://doi.org/10.5194/tc-14-3687-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 14, 3687–3705, 2020
https://doi.org/10.5194/tc-14-3687-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Nov 2020

Research article | 05 Nov 2020

DeepBedMap: a deep neural network for resolving the bed topography of Antarctica

Wei Ji Leong and Huw Joseph Horgan

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (25 Jul 2020) by Olivier Gagliardini
AR by Wei Ji Leong on behalf of the Authors (09 Aug 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (27 Aug 2020) by Olivier Gagliardini
AR by Wei Ji Leong on behalf of the Authors (01 Sep 2020)  Author's response    Manuscript
ED: Publish as is (18 Sep 2020) by Olivier Gagliardini
Publications Copernicus
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Short summary
A machine learning technique similar to the one used to enhance everyday photographs is applied to the problem of getting a better picture of Antarctica's bed – the part which is hidden beneath the ice. By taking hints from what satellites can observe at the ice surface, the novel method learns to generate a rougher bed topography that complements existing approaches, with a result that is able to be used by scientists running fine-scale ice sheet models relevant to predicting future sea levels.
A machine learning technique similar to the one used to enhance everyday photographs is applied...
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