标题： DeepTopoNet：格陵兰冰盖下地形估计的框架 显示英文标题

标题： DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets

摘要： 理解格陵兰岛冰盖下的基底地形对于预测冰盖未来的质量损失及其对全球海平面上升的贡献至关重要。 然而，观测数据的复杂性和稀疏性，尤其是关于冰盖下基底地形的信息，显著增加了模型预测的不确定性。 传统上，基底地形通过机载穿透雷达直接测量飞机正下方的冰厚来测定，导致飞行线之间存在数十公里的数据空白。 本研究介绍了一种深度学习框架，我们称之为DeepTopoNet，它通过一种新颖的动态损失平衡机制整合了雷达衍生的冰厚观测和BedMachine格陵兰数据。 在所有重建基底地形的努力中， BedMachine已成为最广泛使用的数据集之一，结合质量守恒原则和冰厚测量生成高分辨率的基底高程估计。 所提出的损失函数自适应地调整雷达和BedMachine数据之间的权重，在雷达覆盖有限的区域确保鲁棒性，同时利用BedMachine预测的高空间分辨率，即床估计。 我们的方法结合了基于梯度和趋势面特征以增强模型性能，并利用了设计用于亚网格尺度预测的CNN架构。 通过对Upernavik Isstr{\o }m)地区的系统测试，该模型实现了高精度，在重建冰下地形方面优于基准方法。 这项工作展示了深度学习在填补观测空白方面的潜力，提供了一种可扩展且高效的推断冰下地形的解决方案。 显示英文摘要

摘要： 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.