Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction
This work addresses the challenge of improving ice layer thickness predictions for polar ice research, representing an incremental advancement in geometric deep learning for this domain.
The paper tackles the problem of predicting ice layer thickness from noisy echogram images by developing a multi-branch spatio-temporal graph neural network, which outperforms existing fused methods in accuracy and efficiency.
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.