Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network

TL;DR

This paper introduces a physics-informed hybrid graph neural network that effectively models spatio-temporal patterns of polar ice layers, improving predictions of deep ice layer thickness from shallow layer data.

Contribution

It combines GraphSAGE and LSTM with physical ice property measurements to enhance deep ice layer prediction accuracy, a novel integration of physics and graph neural networks.

Findings

Outperforms existing models in predicting deep ice layer thickness.

Incorporates physical measurements to improve model accuracy.

Demonstrates the effectiveness of physics-informed graph neural networks.

Abstract

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.