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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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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
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – KDD '19, ACM Press, Anchorage, AK, USA, https://doi.org/10.1145/3292500.3330701, 2623–2631, 4–8 August 2019. a
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
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B.: Algorithms for Hyper-Parameter Optimization, in: Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS'11, Curran Associates Inc., Granada, Spain, 2546–2554, 2011. a
Bergstra, J., Komer, B., Eliasmith, C., Yamins, D., and Cox, D. D.: Hyperopt: A Python Library for Model Selection and Hyperparameter Optimization, Computational Science & Discovery, 8, 014008, https://doi.org/10.1088/1749-4699/8/1/014008, 2015. a
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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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