Going Deeper With Deep Learning: Automatically Tracing Internal Reflection Horizons in Ice Sheets—Methodology and Benchmark Data Set

Abstract Mapping the internal stratigraphy of ice sheets serves a variety of glaciological applications, from the study of past ice flows to current distribution of surface mass balance and melting to contemporary ice dynamics, all of which are crucial for improving future projections of sea level rise. The method of choice for investigating the internal structure of ice sheets is radio‐echo sounding (RES). Mapping englacial stratigraphy has been carried out so far mostly by time‐consuming manual or semi‐automatic methods. Although reliable, such approaches are not feasible for comprehensive analysis of the wealth of data available. Therefore, (semi‐)automatic mapping of the internal stratigraphy from RES radargrams has been a field of interest for about two decades. Here, our goal is to present a complete pipeline for automatic tracing of internal reflection horizons (IRH) of intermediate to large depths in the ice sheet from radargrams using deep learning. We introduce IRHMapNet, which is a deep learning framework that uses a U‐Net‐based architecture to trace IRHs, based on airborne RES data with preprocessing steps such as noise removal and data augmentation, a