Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography.

Understanding subglacial bed topography is essential for learning about Antarctica in the geologic and glaciological fields. The primary method of investigating the Antarctic bed involves measuring the bed elevation by radio-echo sounding (RES) deployed on aircraft. Digital elevation models (DEMs) of the Antarctic bed generated by traditional interpolation methods usually lack resolution, precision, and roughness. To generate Antarctic bed DEMs by interpolating sparse RES bed elevation data, we use a two-stage coarse-to-fine fully convolutional neural network (CNN), which presents a deep generative elevation inpainting method that can extract, use in-depth features, and reconstruct the bed elevation conforming to the textural character of deglacial landscapes. Our method can generate a detailed and reasonable bed DEM with the full calculation of CNN and the training strategy of a generative adversarial network (GAN). The quantitative evaluation results show that a 250-m resolution elevation grid map with a 77-m mean absolute error (MAE) can be generated through elevation inpainting by sparse data with 4-km RES survey spacing in the Arctic test area. Our study also generates two realistic bed DEMs with a 250-m spatial resolution in the Gamburtsev Subglacial Mountains and Amundsen Sea Embayment. Compared with the existing Antarctic bed DEM products, BedMachine_Antarctica, DeepBedMap_DEM, and MB_DeepBedMap_DEM, our generated bed DEMs show more realistic terrain and elevation with low MAEs in test regions, which could better suit follow-up glaciological research. The code of this work will be available at https://github.com/Hecian/GEI_2022 for the sake of reproducibility.