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

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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1. Physically‐Informed Super‐Resolution Downscaling of Antarctic Surface Melt;Journal of Advances in Modeling Earth Systems;2024-07

2. Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15

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