A Deep Learning Approach for Bottom Detection in Multibeam Water-Column Data via Full-Swath Spatial Context

Bottom detection and tracking from multibeam echosounder water-column data constitute a fundamental step to 3D seabed mapping. Traditional methods based on amplitude thresholding and phase detection rely heavily on heuristic assumptions and are prone to failure in the presence of interference in water-column data. To address these limitations, we propose a novel deep convolutional architecture that models full-swath spatial context, and couple it with a parallelized sliding-window pipeline for end-to-end inference on high-resolution backscatter signals. The proposed model was trained and evaluated on a combined dataset of public deep-water multibeam water-column data acquired by Kongsberg EM302 and EM710 systems. The bottom detection results demonstrate that in deep-water scenarios, our method obtained the correct bottom position, whereas the traditional methods yielded inaccurate or no detection results. Furthermore, applying knowledge distillation yields a lightweight model that achieves a high inference speed on an embedded edge device. The proposed method substantially suppresses interference-induced false detections and provides a robust, real-time solution for marine bathymetry.