Layer-finding in radar echograms using probabilistic graphical models

Ground-penetrating radar systems are useful for a variety scientific studies, including monitoring changes to the polar ice sheets that may give clues to climate change. A key step in analyzing radar echograms is to identify boundaries between layers of material (such as air, ice, rock, etc.). In this paper, we propose an automated technique for identifying these boundaries, posing this as an inference problem on a probabilistic graphical model. We show how to learn model parameters from labeled training data and how to perform inference efficiently, as well as how additional sources of evidence, such as feedback from a human operator, can be naturally incorporated. We evaluate the approach on over 800 echograms of the Antarctic ice sheets, measuring error with respect to hand-labeled ground truth.