Reconstructing Multibeam Echosounder Bathymetry with Generative Adversarial Networks: Toward Efficient Use of Survey Resources

Abstract. The spatial accuracy and resolution of Multibeam Echosounder data are inherently lower than those of high-resolution underwater LiDAR measurements. However, while Multibeam Echosounder provides wide coverage and extensive historical availability, LiDAR is costly and covers relatively small areas. In this study, we propose an innovative approach to enhance Multibeam Echosounder resolution using a Super-Resolution Generative Adversarial Network with direct comparison to LiDAR data for accuracy assessment. The methodology involves converting Multibeam Echosounder data into grayscale format using various depth gradient techniques, analyzing differences in submarine geomorphology through calculations of slope and aspect, and evaluating statistical accuracy. The results show that the Super-Resolution Generative Adversarial Network model successfully improves Multibeam Echosounder resolution, producing data that closely correspond to LiDAR measurements, particularly in flat, sandy seabed areas. In contrast, regions with complex or rocky terrain exhibited more pronounced deviations, especially in aspect metrics, emphasizing the challenges associated with maintaining topographic orientation throughout the resolution enhancement process. The main conclusion is that enhancing Multibeam Echosounder data using Super-Resolution Generative Adversarial Network enables broader utilization of existing datasets to generate high-resolution models, offering a more cost-effective and accurate solution for seafloor mapping in areas where LiDAR data are unavailable.