Near-Surface Crevasse Detection in Ice Sheets using Feature-Based Machine Learning

In 2014, a team of Dartmouth, CRREL, and University of Maine researchers conducted the first of three annual ground-penetrating radar surveys of the McMurdo Shear Zone using robot-towed instruments. This survey provides over 100 transects of a 5.7 km x 5.0 km grid spanning the width of the shear zone at spacing of approximately 50 m. Transect direction was orthogonal to ice flow. Additionally, a dense 200 m x 200 m grid was surveyed at 10 m spacing in both the N-S and W-E directions. Radar settings provided 20 traces/sec, which combined with an average robot speed of 1.52 m/s, provides a trace every 7.6 cm. The robot towed two antenna units at 400 MHz and 200 MHz center frequencies, with the former penetrating to approximately 19 m. We establish boundaries for the shear zone over the region surveyed using the 400 MHz antenna data, and we geo-locate crevasses using feature-based machine learning classification of GPR traces into one of three classes - 1) firn, 2) distinct crevasses, and 3) less distinct or deeper features originating within the 19 m penetration depth. Distinct crevasses feature wide, hyperbolic reflections with strike angles of 35-40° to transect direction and clear voids. Less distinct or deeper features range from broad diffraction patterns with no clear void to overlapping diffractions extending tens of meters in width with or without a clear void. The classification is derived from statistical features of unprocessed traces and thus provides a computationally efficient means for eventual real-time classification of GPR traces. Feature-based classification is shown to be insensitive to artifacts related to rolling or pitching motion of the instrument sled and also provides a means of assessing crevasse width and depth. In subsequent years, we will use feature-based classification to estimate ice flow and evolution of individual crevasses.