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.

翻译：精确的格陵兰冰下床地形图对海平面预测至关重要，但雷达观测数据稀疏且分布不均。本文提出GraphTopoNet——一种融合异质监督并通过蒙特卡洛随机失活显式建模不确定性的图学习框架。基于地表观测变量（高程、流速、物质平衡）构建的空间图，通过梯度特征与多项式趋势增强，以同时捕捉局部变异性和宏观结构。为处理数据缺失问题，我们采用混合损失函数，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表明，GraphTopoNet在插值法、卷积网络及图基线方法中表现最优，误差降低达60%，同时保持冰川细微特征。所得冰床地形图提升了业务化建模的可靠性，为气候预测与政策制定机构提供支持。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操作知识。