  Learning spatio-temporal patterns of polar ice layers is crucial for
monitoring the change in ice sheet balance and evaluating ice dynamic
processes. While a few researchers focus on learning ice layer patterns from
echogram images captured by airborne snow radar sensors via different
convolutional neural networks, the noise in the echogram images proves to be a
major obstacle. Instead, we focus on geometric deep learning based on graph
neural networks to learn the spatio-temporal patterns from thickness
information of shallow ice layers and make predictions for deep layers. In this
paper, we propose a physics-informed hybrid graph neural network that combines
the GraphSAGE framework for graph feature learning with the long short-term
memory (LSTM) structure for learning temporal changes, and introduce
measurements of physical ice properties from Model Atmospheric Regional (MAR)
weather model as physical node features. We found that our proposed network can
consistently outperform the current non-inductive or non-physical model in
predicting deep ice layer thickness.
