First author article in Artificial Intelligence in Geosciences

Enhancing coarse bathymetric grids using a generative model: An arctic case study

Jan Matwiejczuk, Matthew Danielson, Jens Jasche, Larry A. Mayer, Rezwan Mohammad, Thi Tam Truong, and Martin Jakobsson, 2026. Enhancing coarse bathymetric grids using a generative model: An arctic case study. Artificial Intelligence in Geosciences: 7, 100284. https://doi.org/10.1016/j.aiig.2026.100284

Abstract
Bathymetric mapping of ice-covered polar seas remains a major challenge. Persistent sea ice restricts systematic surveys, while icebreaking operations degrade multibeam echosounder (MBES) data quality through noise, hull vibrations, and ice interference. As a result, digital terrain models (DTMs) from bathymetric surveys in ice-covered regions are often fragmented, coarse, and incomplete, limiting interpretation of seafloor morphology, including glacial landforms used to infer former glacier extent and ice-sheet dynamics. Conventional interpolation for upscaling and gap filling often oversmooths morphology and suppresses geomorphologically important landforms. We developed a generative super-resolution approach to reconstruct bathymetry at 25 m resolution from degraded 100 m and 200 m inputs (4× and 8× super-resolution). We additionally examine whether fine-scale glacial bathymetric structure can be inferred from coarser geomorphological context. To address the issue of limited bathymetric training data from polar regions, we trained a conditional diffusion model on high-quality terrestrial DTMs containing landforms analogous to glaciated submarine settings. The model was evaluated on held-out Arctic MBES bathymetry from offshore northern Greenland using patch-based tests, full-grid evaluation, and qualitative assessment. Compared to bicubic interpolation, the model improves elevation-distribution agreement, feature-space similarity, and spectral consistency. However, gains in spatial cross-correlation remain limited, suggesting that coarse geomorphological context alone may provide insufficient information for reliable inference of fine-scale landforms. Qualitative assessment suggests clearer expression of glacial landforms, including lineations and retreat moraines, where only weak traces were visible in coarse bathymetry. These results suggest that proxy-trained generative super-resolution can serve as an interpretation-enhancing post-processing tool for degraded Arctic bathymetric grids.