A Semiautomatic Method for Predicting Subglacial Dry and Wet Zones Through Identifying Dry–Wet Transitions

Abstract

In the past decades, radio-echo sounding (RES) data have been used to predict basal dry-wet distributions in glaciated regions through manual inspection of the records. Extending such work, we propose a semi-automatic method for predicting such distributions. The method improves previous work in two ways: (1) subglacial water bodies are taken as reference to correct the thresholds of dry and wet beds identification at a regional scale; and (2) five distinct features are defined and used to automatically identify the dry-wet transition, allowing a classification model based on a support vector machine. To demonstrate its effectiveness, the method is applied to airborne RES data collected in recent years over Princess Elizabeth Land in East Antarctica. A comparative analysis of the new vs previous method was carried out in the Ridge B region of the East Antarctica and at the Thwaites Glacier region of West Antarctica. The results show the method can obtain more accurate subglacial dry-wet distribution results with larger coverage and has the potential to determine dry-wet transitions at a continental scale if applied to the full set of known Antarctic RES data.