用物理约束提升稀疏雷达数据的冰下地形重建精度
Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
- 基于先验地形，通过物理引导残差学习预测冰厚
- 在格陵兰两个区域测试中优于多种主流网络模型
- 适合冰盖建模、气候变化研究等需要可靠地形数据的场景
精确的冰下基底地形对冰盖模拟至关重要，但雷达观测稀疏且分布不均。本文提出一种物理引导的残差学习框架，通过在BedMachine先验基础上预测冰厚残差并重构基底。采用DeepLabV3+解码器与标准编码器（如ResNet-50），结合轻量级物理项与数据项：多尺度质量守恒、流线对齐总变差、拉普拉斯阻尼、厚度非负性、渐进式先验一致性项，以及基于置信度图调制的掩码Huber损失。为评估真实泛化能力，采用带安全缓冲区的块状留出法（垂直/水平方向），仅在保留核心区域报告指标。在格陵兰两个子区域上，本方法实现高测试核心精度与良好结构保真度，优于U-Net、Attention U-Net、FPN及普通CNN。残差-先验设计结合物理约束，生成空间连贯、物理解释性强的基底地形，适用于领域偏移下的实际映射任务。
原文摘要 · Abstract (English)
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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