| Summary: | ArtÃculos en revistas Englacial layering reflects ice dynamics within the ice bodies, which improves understanding of ice flow variation, past accumulation rates and vertical flows transferring between the surface and the underlying bedrock. The internal layers can be observed by using Radar Echo Sounding (RES), such as the Multi-channel Coherent Radar Depth Sounder (MCoRDS) used in NASA s Operation IceBridge (OIB) mission. Since the 1960s, the accumulation of the RES data has prompted the development of automated methods to extract the englacial layers. In this study, we propose a new automated method that combines peak detection methods, namely the CWT-based peak detection or the Automatic Phase Picker (APP), with a Hough Transform (HT) to trace boundaries of englacial layers. For CWT-based peak detection, we test it using two different wavelets. The proposed method is tested with twelve MCoRDS radio echograms, which are acquired south of the Northern Greenland Eemian (NEEM) ice drilling site, where the folding of ice layers was observed. The method is evaluated in comparison to the isochrones that were extracted in an independent study. In comparison, the proposed new automated method can restore more than 70% of the englacial layers. This new automated layer-tracing method is publicly available on github Englacial layering reflects ice dynamics within the ice bodies, which improves understanding of ice flow variation, past accumulation rates and vertical flows transferring between the surface and the underlying bedrock. The internal layers can be observed by using Radar Echo Sounding (RES), such as the Multi-channel Coherent Radar Depth Sounder (MCoRDS) used in NASA s Operation IceBridge (OIB) mission. Since the 1960s, the accumulation of the RES data has prompted the development of automated methods to extract the englacial layers. In this study, we propose a new automated method that combines peak detection methods, namely the CWT-based peak detection or the Automatic Phase Picker (APP), with a Hough ...
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