The evolution of the Antarctic ice sheets depends, in addition to factors representing the warming climate, on the earth structure beneath the ice. What’s beneath the ice is largely inaccessible for direct sampling, but can be interpreted with the use of airborne measurements. We apply an unsupervised machine learning method to such data in East Antarctica to test whether this can ease interpretation and hence our understanding of what rocks types are beneath the ice.