Physics-informed Machine Learning for Deep Ice Layer Tracing in SAR images

The precise prediction and tracking of deep internal layers of ice sheets is becoming increasingly important as we deal with the impacts of climate change and the rise of global atmospheric temperatures. Synthetic Aperture Radar (SAR) is the only sensor capable of penetrating through ice and providing us with information about what lies beneath the ice surface, allowing us to monitor changes on a large scale. Forecasting and tracking these internal ice sheet layers is crucial for calculating snow mass balance, inferring otherwise difficult-to-observe ice dynamic processes, and extrapolating ice age from direct measurements of the subsurface. To achieve this, we developed a geometric deep learning model that uses a supervised, multi-target, adaptive long short-term memory graph convolutional network to predict the thicknesses of multiple deep ice layers at specific coordinates in an ice sheet given the thicknesses of a few shallow ice layers. Furthermore, we expanded the model to consider additional physical features of the ice, alongside layer thickness. We found that the inclusion of snow mass balance, meltwater refreezing, and height change due to melting as node features give our model better and more consistent performance.