A technique for the automatic estimation of ice thickness and bedrock properties from radar sounder data acquired at Antarctica

The rise in the temperature of the planet has a very negative impact on the subsurface dynamics of the Earth Polar Regions. This makes it crucial to investigate the features present in the ice subsurface. The analysis of these features is typically performed manually, by examining radargrams acquired by radar sounder (RS) instruments operated at the Polar Caps. However, in order to cope with the very large amount of data that RSs can acquire, it is necessary to develop data analysis techniques that can identify and extract subsurface features automatically. To address this problem, in this paper we propose a novel technique for the automatic estimation of the ice thickness and bedrock properties from RS data acquired in Antarctica. The proposed technique generates a statistical map of the subsurface by exploiting the statistical properties of the radar signal. Then, it applies a segmentation algorithm properly tuned to the characteristics of the investigated areas. In order to assess the effectiveness of the proposed technique, we analyzed its performance when applied to Multichannel Coherent Radar Depth Sounder (MCoRDS) data acquired in Antarctica.