# Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction

arXiv: https://arxiv.org/abs/2411.04055 · HTML: https://mlsanity.com/paper/2411.04055/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [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)
- [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)
- [Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network](https://mlsanity.com/paper/2406.15299/) (2024)

## Built on by (most cited first, 5 of 5)

- [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)
- [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-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces](https://mlsanity.com/paper/2604.20783/) (2026)

## Abstract (arXiv)

Understanding spatio-temporal patterns in polar ice layers is essential for tracking changes in ice sheet balance and assessing ice dynamics. While convolutional neural networks are widely used in learning ice layer patterns from raw echogram images captured by airborne snow radar sensors, noise in the echogram images prevents researchers from getting high-quality results. Instead, we focus on geometric deep learning using graph neural networks, aiming to build a spatio-temporal graph neural network that learns from thickness information of the top ice layers and predicts for deeper layers. In this paper, we developed a novel multi-branch spatio-temporal graph neural network that used the GraphSAGE framework for spatio features learning and a temporal convolution operation to capture temporal changes, enabling different branches of the network to be more specialized and focusing on a single learning task. We found that our proposed multi-branch network can consistently outperform the current fused spatio-temporal graph neural network in both accuracy and efficiency.