# Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks

arXiv: https://arxiv.org/abs/2306.13690 · HTML: https://mlsanity.com/paper/2306.13690/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks](https://mlsanity.com/paper/2009.00191/) (2020)
- [Multistream Graph Attention Networks for Wind Speed Forecasting](https://mlsanity.com/paper/2108.07063/) (2021)
- [Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar](https://mlsanity.com/paper/2302.00817/) (2023)

## Built on by (most cited first, 8 of 8)

- [Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network](https://mlsanity.com/paper/2406.15299/) (2024)
- [Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction](https://mlsanity.com/paper/2411.04055/) (2024)
- [GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction](https://mlsanity.com/paper/2507.07388/) (2025)
- [K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data](https://mlsanity.com/paper/2604.09922/) (2026)
- [GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Prediction](https://mlsanity.com/paper/2511.18716/) (2025)
- [PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation](https://mlsanity.com/paper/2605.09036/) (2026)
- [Physics-Informed Machine Learning On Polar Ice: A Survey](https://mlsanity.com/paper/2404.19536/) (2024)
- [Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces](https://mlsanity.com/paper/2604.20783/) (2026)

## Abstract (arXiv)

As we deal with the effects of climate change and the increase of global atmospheric temperatures, the accurate tracking and prediction of ice layers within polar ice sheets grows in importance. Studying these ice layers reveals climate trends, how snowfall has changed over time, and the trajectory of future climate and precipitation. In this paper, we propose a machine learning model that uses adaptive, recurrent graph convolutional networks to, when given the amount of snow accumulation in recent years gathered through airborne radar data, predict historic snow accumulation by way of the thickness of deep ice layers. We found that our model performs better and with greater consistency than our previous model as well as equivalent non-temporal, non-geometric, and non-adaptive models.