用图学习提升格陵兰冰下地形图精度，处理稀疏雷达数据不确定性。
Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data
- 构建基于地表观测的图结构，融合梯度与多项式趋势捕捉冰层特征。
- 通过蒙特卡洛丢弃建模不确定性，误差降低最高达60%。
- 适合气候建模与政策制定者使用，可推广至其他地质领域。
准确绘制格陵兰冰下床面地形对海平面上升预测至关重要，但雷达观测稀疏且分布不均。我们提出GraphTopoNet，一种图学习框架，通过蒙特卡洛丢弃显式建模不确定性，并融合异构监督信号。基于地表可观测量（高程、速度、质量平衡）构建空间图，引入梯度特征与多项式趋势以捕捉局部变化与整体结构。为应对数据缺失，采用混合损失函数，结合置信度加权的雷达监督与动态平衡正则化。在格陵兰三个子区域的应用中，GraphTopoNet优于插值法、卷积网络及图基基线方法，误差最高降低60%，同时保留细尺度冰川特征。生成的床面地图提升了业务模型可靠性，助力气候预测与政策制定机构。更广泛而言，GraphTopoNet展示了图机器学习如何将稀疏、不确定的地球物理观测转化为大陆尺度的可用知识。
原文摘要 · Abstract (English)
Accurate maps of Greenland's subglacial bed are essential for sea-level projections, but radar observations are sparse and uneven. We introduce GraphTopoNet, a graph-learning framework that fuses heterogeneous supervision and explicitly models uncertainty via Monte Carlo dropout. Spatial graphs built from surface observables (elevation, velocity, mass balance) are augmented with gradient features and polynomial trends to capture both local variability and broad structure. To handle data gaps, we employ a hybrid loss that combines confidence-weighted radar supervision with dynamically balanced regularization. Applied to three Greenland subregions, GraphTopoNet outperforms interpolation, convolutional, and graph-based baselines, reducing error by up to 60 percent while preserving fine-scale glacial features. The resulting bed maps improve reliability for operational modeling, supporting agencies engaged in climate forecasting and policy. More broadly, GraphTopoNet shows how graph machine learning can convert sparse, uncertain geophysical observations into actionable knowledge at continental scale.
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