Inferring the properties and history of Antarctic Ice Sheets with Machine Learning
Glaciologists investigate the ice sheets and ice shelves of Antarctica using a variety of methods, key among them is radio-echo sounding. This method allows glaciologists to measure the reflections of emitted radar waves from the internal layers of the ice body and from the interface between ice and underlying material. How can we use this data to extract information about the historical climate conditions, as well as the properties of the ice and what lies below it? We investigate this question together with collaborators in the glaciology and geophysics group led by Prof. Reinhard Drews at the University of Tübingen. We develop approaches combining the physical modelling of processes within the ice sheets, together with machine learning-enabled simulation-based inference to tackle this question. This provides uncertainty-aware predictions about the state of the ice sheet. Photo from Glaciology & Geophysics Tübingen.