DeepBedMap: a deep neural network for resolving the bed topography of Antarctica
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Published:2020-11-05
Issue:11
Volume:14
Page:3687-3705
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ISSN:1994-0424
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Container-title:The Cryosphere
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language:en
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Short-container-title:The Cryosphere
Author:
Leong Wei JiORCID, Horgan Huw Joseph
Abstract
Abstract. To resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that can produce Antarctic bed topography with
adequate surface roughness from multiple remote sensing data inputs. The super-resolution deep convolutional neural network model is trained on
scattered regions in Antarctica where high-resolution (250 m) ground-truth bed elevation grids are available. This model is then used to
generate high-resolution bed topography in less surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a
low-spatial-resolution (1000 m) BEDMAP2 raster image as its prior image. It takes in additional high-spatial-resolution datasets, such as ice
surface elevation, velocity and snow accumulation, to better inform the bed topography even in the absence of ice thickness data from direct
ice-penetrating-radar surveys. The DeepBedMap model is based on an adapted architecture of the Enhanced Super-Resolution Generative Adversarial Network,
chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four-times-upsampled (250 m) bed
elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain and by ice sheet modellers wanting to run
catchment- or continent-scale ice sheet model simulations. We show that DeepBedMap offers a rougher topographic profile compared to the standard
bicubically interpolated BEDMAP2 and BedMachine Antarctica and envision it being used where a high-resolution bed elevation model is required.
Funder
Royal Society of New Zealand
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Water Science and Technology
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