Multibeam echosounders have revolutionized underwater exploration, enabling the study of biological and geophysical phenomena such as fish distribution and gas venting. We employed automated deep-learning methods to analyze the large volume of water-column data, thus obtaining a big database of water-column echoes of different types. The subsequent objective is to ascertain the relationships between these echoes. Consequently, it is imperative to reintegrate physical principles into our analytical framework.

Our research addresses this gap with a novel approach to tracking water-column echoes combining multibeam simulation, to account for the geometry of beam directivity and sidelobe effects, with graph representation to model relationships between echoes. This method allows to track and cluster water-column echoes by bridging simulation, graph representation, and acoustic physics. This method identifies whether an echo is a ghost or other echo by (i) approximating the mean sidelobe levels under a simplified hypothesis (ii) simulating the along/across-track positions and levels from an echo detected with an automatic detector and a multibeam survey toolbox and (iii) analyzing geometric and level relationships between echoes from simulated acoustic point clouds. The method then (iv) represents these relationships in the form of a graph that respects multibeam sounder directivity. Importantly, this approach bypasses the need to accurately reproduce sidelobe positions in the directivity pattern, focusing instead on relative acoustic levels and geometric relationships between echoes.

We tested this approach on different multibeam surveys to ensure the generalizability of this method. Results demonstrate that our method works effectively in a range of contexts, including echoes of a single target from successive WCIs, ghost echoes in the across and along axes, and multiple distinct target echoes within the same water-column image. 

This dataset contains videos for the results of the conference article [Target tracking in multibeam water-column images based on acoustic simulation of beam forming artifacts T. Perret, G. Le Chenadec, A. Gaillot, Y. Ladroit and S. Dupré (2025) IEEE OCEANS 2025 Brest]. These videos show the water-column echoes used and the related components of the final graph. The speed of the videos is set to 1 ping (one frame) per second except for the frame in the GHASS2 dataset where we can see the ghost echoes in the across-track axis.