Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals

• URL: http://arxiv.org/abs/2511.14473v1
• Date: Tue, 18 Nov 2025 13:12:58 GMT
• Title: Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
• Authors: Bayu Adhi Tama, Jianwu Wang, Vandana Janeja, Mostafa Cham,
• Abstract summary: We propose a physics-guided residual learning framework that predicts bed thickness residuals over a BedMachine prior.<n>A DeepLabV3+ decoder over a standard encoder (e.g.,ResNet-50) is trained with lightweight physics and data terms.<n>Across two Greenland sub-regions, our approach achieves strong test-core accuracy and high structural fidelity.
• Score: 1.912667743759162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate subglacial bed topography is essential for ice sheet modeling, yet radar observations are sparse and uneven. We propose a physics-guided residual learning framework that predicts bed thickness residuals over a BedMachine prior and reconstructs bed from the observed surface. A DeepLabV3+ decoder over a standard encoder (e.g.,ResNet-50) is trained with lightweight physics and data terms: multi-scale mass conservation, flow-aligned total variation, Laplacian damping, non-negativity of thickness, a ramped prior-consistency term, and a masked Huber fit to radar picks modulated by a confidence map. To measure real-world generalization, we adopt leakage-safe blockwise hold-outs (vertical/horizontal) with safety buffers and report metrics only on held-out cores. Across two Greenland sub-regions, our approach achieves strong test-core accuracy and high structural fidelity, outperforming U-Net, Attention U-Net, FPN, and a plain CNN. The residual-over-prior design, combined with physics, yields spatially coherent, physically plausible beds suitable for operational mapping under domain shift.

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