Articles | Volume 14, issue 11
https://doi.org/10.5194/tc-14-3687-2020
https://doi.org/10.5194/tc-14-3687-2020
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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Cited articles

Aitken, A. R. A., Young, D. A., Ferraccioli, F., Betts, P. G., Greenbaum, J. S., Richter, T. G., Roberts, J. L., Blankenship, D. D., and Siegert, M. J.: The subglacial geology of Wilkes Land, East Antarctica, Geophys. Res. Lett., 41, 2390–2400, https://doi.org/10.1002/2014GL059405, 2014. a
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Arthern, R. J., Winebrenner, D. P., and Vaughan, D. G.: Antarctic snow accumulation mapped using polarization of 4.3-cm wavelength microwave emission, J. Geophys. Res., 111, D06107, https://doi.org/10.1029/2004JD005667, 2006. a, b, c, d
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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.