DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
Understanding Greenland's subglacial topography is critical for projecting
the future mass loss of the ice sheet and its contribution to global sea-level
rise. However, the complex and sparse nature of observational data,
particularly information about the bed topography under the ice sheet,
significantly increases the uncertainty in model projections. Bed topography is
traditionally measured by airborne ice-penetrating radar that measures the ice
thickness directly underneath the aircraft, leaving data gap of tens of
kilometers in between flight lines. This study introduces a deep learning
framework, which we call as DeepTopoNet, that integrates radar-derived ice
thickness observations and BedMachine Greenland data through a novel dynamic
loss-balancing mechanism. Among all efforts to reconstruct bed topography,
BedMachine has emerged as one of the most widely used datasets, combining mass
conservation principles and ice thickness measurements to generate
high-resolution bed elevation estimates. The proposed loss function adaptively
adjusts the weighting between radar and BedMachine data, ensuring robustness in
areas with limited radar coverage while leveraging the high spatial resolution
of BedMachine predictions i.e. bed estimates. Our approach incorporates
gradient-based and trend surface features to enhance model performance and
utilizes a CNN architecture designed for subgrid-scale predictions. By
systematically testing on the Upernavik Isstr{\o}m) region, the model achieves
high accuracy, outperforming baseline methods in reconstructing subglacial
terrain. This work demonstrates the potential of deep learning in bridging
observational gaps, providing a scalable and efficient solution to inferring
subglacial topography.
