# Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces

arXiv: https://arxiv.org/abs/2604.20783 · HTML: https://mlsanity.com/paper/2604.20783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar](https://mlsanity.com/paper/2302.00817/) (2023)
- [Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach](https://mlsanity.com/paper/2306.13181/) (2023)
- [Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks](https://mlsanity.com/paper/2306.13690/) (2023)
- [Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction](https://mlsanity.com/paper/2411.04055/) (2024)
- [ST-GRIT: Spatio-Temporal Graph Transformer For Internal Ice Layer Thickness Prediction](https://mlsanity.com/paper/2507.07389/) (2025)
- [GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction](https://mlsanity.com/paper/2507.07388/) (2025)
- [AI-ready Snow Radar Echogram Dataset (SRED) for climate change monitoring](https://mlsanity.com/paper/2505.00786/) (2025)

## Abstract (arXiv)

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.