Deep clustering in subglacial radar reflectance reveals subglacial lakes

Abstract

Ice-penetrating radar (IPR) imaging is a valuable tool for observing the internal structure and bottom of ice sheets. Subglacial water bodies, also known as subglacial lakes, generally appear as distinct, bright, flat, and continuous reflections in IPR images. In this study, we use available IPR images from the Gamburtsev Subglacial Mountains to extract one-dimensional reflector waveform features of the ice-bedrock interface. We apply a deep-learning method to reduce the dimension of the reflector features. An unsupervised clustering method is then used to separate different types of reflector features, including a reflector type corresponding to subglacial lakes. The derived clustering labels are then used to detect features of subglacial lakes in IPR images. Using this method, we compare the new detections with a known-lakes inventory. The results indicate that this new method identified additional subglacial lakes that were not previously detected, and some previously known lakes are found to correspond to other reflector clusters. This method can offer automatic detections of subglacial lakes and provide new insight for subglacial studies.