用图注意力网络预测冰层厚度，提升气候模型精度
GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction
- 构建图注意力模型，捕捉浅层与深层冰层关系
- 相比基线模型误差更低，有效捕捉冰层时序变化
- 适合冰雪监测与气候建模研究者使用
深入理解雷达图像中内部冰层的厚度与变化，对监测雪积累、评估冰体动态过程以及降低气候模型不确定性至关重要。雷达传感器可穿透冰层，获取内部冰层的详细剖面图像。本文提出GRIT（Graph Transformer for Internal Ice Layer Thickness），一种结合归纳几何图学习框架与注意力机制的模型，用于建模浅层与深层冰层间的关联关系。相较于基线图神经网络，GRIT表现出更一致的低预测误差。结果表明，注意力机制能有效捕捉冰层随时间的变化特征；图变压器融合了变换器对长程依赖建模的优势与图神经网络对空间模式的捕捉能力，实现了对复杂时空动态的稳健建模。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。