Internal ice layers imaged by radar provide key evidence of snow accumulation and ice dynamics, but radar-derived layer boundary observations are often incomplete, with discontinuous traces and sometimes entirely missing layers, due to limited resolution, sensor noise, and signal loss. Existing graph-based models for ice stratigraphy generally assume sufficiently complete layer profiles and focus on predicting deeper-layer thickness from reliably traced shallow layers. In this work, we address the layer-completion problem itself by synthesizing complete ice-layer thickness annotations from incomplete radar-derived layer traces by conditioning on colocated physical features synchronized from physical climate models. The proposed network combines geometric learning to aggregate within-layer spatial context with a transformer-based temporal module that propagates information across layers to encourage coherent stratigraphy and consistent thickness evolution. To learn from incomplete supervision, we optimize a mask-aware robust regression objective that evaluates errors only at observed thickness values and normalizes by the number of valid entries, enabling stable training under varying sparsity without imputation and steering completions toward physically plausible values. The model preserves observed thickness where available and infers only missing regions, recovering fragmented segments and even fully absent layers while remaining consistent with measured traces. As an additional benefit, the synthesized thickness stacks provide effective pretraining supervision for a downstream deep-layer predictor, improving fine-tuned accuracy over training from scratch on the same fully traced data.

翻译：雷达成像揭示的内部冰层是积雪累积和冰层动力学的关键证据，但由于分辨率限制、传感器噪声及信号损失，雷达导出的层界观测结果常存在不连续性——表现为迹线断裂甚至整层缺失。现有基于图的冰层结构模型通常假设层剖面足够完整，并侧重于根据可靠追踪的浅层厚度预测深层厚度。本研究针对层位补全问题本身，通过同步物理气候模型的空间共位物理特征约束，从雷达导出的不完整层迹中合成完整的冰层厚度标注。所提网络融合了几何学习（聚合层内空间上下文）与基于Transformer的时间模块（跨层传播信息以促进层理连贯性与厚度演变一致性）。为实现基于不完整标注的学习，我们优化了掩膜感知的鲁棒回归目标——该目标仅在观测厚度值处计算误差并通过有效条目数进行归一化，从而无需插值即可在稀疏度可变条件下实现稳定训练，并将补全结果导向物理可行值。模型保留可观测厚度并仅推断缺失区域，在保持与实测迹线一致性的同时修复断裂片段甚至补全完全缺失的层位。作为附加优势，合成的厚度堆栈可作为下游深层厚度预测器的有效预训练监督，相较在相同全追踪数据上从零训练，显著提升微调精度。