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.
Highlights
The bed of the Antarctic ice sheet is one of the most challenging surfaces on Earth to map due to the thick layer of ice cover
A need for a higher-spatial-resolution digital elevation model (DEM) is apparent, as ice sheet models move to using sub-kilometre grids in order to quantify glacier ice flow dynamics more accurately (Le Brocq et al, 2010; Graham et al, 2017)
We present the output digital elevation model (DEM) of the super-resolution DeepBedMap neural network model and compare it with bed topography produced by other methods
Summary
The bed of the Antarctic ice sheet is one of the most challenging surfaces on Earth to map due to the thick layer of ice cover. The Antarctic ice sheet is estimated to hold a sea level equivalent (SLE) of 57.9 ± 0.9 m (Morlighem et al, 2019). Especially at short wavelengths, exerts a frictional force against the flow of ice, making it an important influence on ice velocity (Bingham et al, 2017; Falcini et al, 2018). A need for a higher-spatial-resolution digital elevation model (DEM) is apparent, as ice sheet models move to using sub-kilometre grids in order to quantify glacier ice flow dynamics more accurately (Le Brocq et al, 2010; Graham et al, 2017).
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