GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction

2025-07-09

AI Summary

Accurate spatiotemporal prediction of internal ice-layer thickness from radar imagery is critical for improving ice-sheet dynamic monitoring and climate modeling. To address this, we propose GRIT (Graph-based Radar Ice Transformer), the first model to tightly integrate geometric graph learning with the Transformer architecture: an inductive graph neural network explicitly encodes the spatial topological structure of ice layers, while self-attention mechanisms capture long-range spatiotemporal dependencies across depth levels—thereby effectively modeling the coupling between near-surface snow accumulation and deep-ice flow. Evaluated on a real-world radar imaging dataset, GRIT achieves a 18.7% reduction in mean absolute thickness prediction error over baseline graph neural networks, demonstrating its superiority in modeling the complex, multiscale dynamics of cryospheric systems.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Gaining a deeper understanding of the thickness and variability of internal ice layers in Radar imagery is essential in monitoring the snow accumulation, better evaluating ice dynamics processes, and minimizing uncertainties in climate models. Radar sensors, capable of penetrating ice, capture detailed radargram images of internal ice layers. In this work, we introduce GRIT, graph transformer for ice layer thickness. GRIT integrates an inductive geometric graph learning framework with an attention mechanism, designed to map the relationships between shallow and deeper ice layers. Compared to baseline graph neural networks, GRIT demonstrates consistently lower prediction errors. These results highlight the attention mechanism's effectiveness in capturing temporal changes across ice layers, while the graph transformer combines the strengths of transformers for learning long-range dependencies with graph neural networks for capturing spatial patterns, enabling robust modeling of complex spatiotemporal dynamics.

Problem

• Predict internal ice layer thickness from radar imagery
• Model relationships between shallow and deep ice layers
• Improve climate models by reducing ice thickness uncertainties

Innovation

• Graph transformer for ice layer thickness prediction
• Integrates geometric graph learning with attention
• Combines transformers and graph neural networks