Diffusion-based Super-Resolution of Arctic Bathymetry for Glacial Geomorphology

Bathymetric mapping of the Arctic seafloor remains challenging due to persistent sea ice, which limits systematic surveys and degrades multibeam echosounder (MBES) data through reduced swath width, ice interference, and vessel-induced noise. As a result, Digital Terrain Models (DTMs) derived from MBES data in ice-covered regions are often fragmented, coarse, and incomplete, obscuring bottom morphological features. These includes submarine glacial landforms that are informative of past glacier extents and ice-sheet dynamics. Standard interpolation is commonly used for upsampling and gap-filling but systematically oversmooths seafloor morphology, removing the small-scale variability central to glacial geomorphological interpretation.

Here, we investigate whether domain-informed generative super-resolution can recover geomorphologically meaningful structure in degraded Arctic bathymetry. We target upscaling from 100–200 m grid cell resolution to 25 m (4×–8×), with explicit emphasis on preserving glacial landforms rather than optimizing pixel-wise fidelity. We compile (i) 25 m MBES-derived bathymetry from surveys near northern Greenland and around Svalbard, which is downsampled to 100 m and 200 m to create controlled low-resolution inputs, and (ii) a larger set of terrestrial post-glacial Digital Elevation Models (DEMs) from Norway, Iceland, and the Hudson Bay region derived from airborne LiDAR and satellite products. The terrestrial DEMs provide a geomorphological prior without hydroacoustic artifacts and are used for training, while MBES data are reserved exclusively for evaluation in the Arctic bathymetry use case, acknowledging the domain shift between terrestrial and submarine environments.

We train a conditional diffusion model with a U-Net backbone to generate 25 m terrain conditioned on low-resolution inputs. In controlled downsampling experiments, conventional super-resolution metrics show limited separation from deterministic baselines; however, distributional similarity, quantified using the Wasserstein distance of elevation-value distributions, consistently improves. Qualitative assessments in regions such as Svalbard, Nares Strait, and Victoria Fjord show that the diffusion model produces sharper glacial lineations, more distinct retreat moraines, and clearer iceberg scour patterns than interpolation-based methods. To better quantify these geomorphological improvements, we introduce a Fourier-domain evaluation based on radial power spectral density and cross-correlation. Frequency-domain analysis shows that diffusion outputs more closely match the spectral characteristics of the 25 m reference data and tend to restore mid-wavelength power associated with glacial bedforms. Overall, the results suggest that domain-informed generative super-resolution can produce more interpretable bathymetric grids, while underscoring the need for evaluation metrics aligned with geomorphological realism.