ST-SOLOv2: Tracing Depth Hoar Layers in Antarctic Ice Sheet From Airborne Radar Echograms With Deep Learning

Depth hoar (DH) forms when there is a strong temperature gradient in the snowpack. In the Antarctic ice sheet (AIS), DH mostly forms during summer insolation. The seasonal regularity provides an age marker for each internal snow/firn layer, which is necessary for estimating surface mass balance (SMB) using ice-penetrating radar (IPR) and ice core. However, little is known about DH inside the AIS because ice drill and snow pit observations are inefficient and sparse. The deployment of the Operation IceBridge (OIB) airborne snow radar has significantly enhanced the field observation dataset. So far, the spatial distribution of DH remains unclear due to the lack of DH extraction over the AIS from OIB snow radar. Based on instance segmentation SOLOv2 and self-attention mechanism Swin Transformer, a DH layer automatic extraction algorithm ST-SOLOv2 is proposed, with AP50 of 0.9 and F1-score of 0.83, outperforms other commonly used instance segmentation networks (SOLOv2, Mask R-CNN, BlendMask, YOLACT, and CondInst). After the proposed preprocessing pipeline, we conduct the ST-SOLOv2 to locate each DH near the surface. Our results suggest DH number increases with elevation and decreases with slope angle from coast to inland. And DH number decreases with surface melting days as melting/freezing cycles obscure snow layering. We present a method for efficiently monitoring the DH distribution that can be used in further studies of snow radar SMB estimation.