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ORIGINAL RESEARCH article

Front. Remote Sens.
Sec. Acoustic Remote Sensing
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1532714
This article is part of the Research Topic Multibeam Echosounder Backscatter: Advances and Applications View all 4 articles

Deep-learning-based detection of underwater fluids in multiple multibeam echosounder data

Provisionally accepted
Tyméa Perret Tyméa Perret 1*Gilles Le Chenadec Gilles Le Chenadec 2Arnaud Gaillot Arnaud Gaillot 3Yoann Ladroit Yoann Ladroit 4Stéphanie Dupré Stéphanie Dupré 1
  • 1 Ifremer, Geo-Ocean, Plouzané, France
  • 2 ENSTA Bretagne, Lab-STICC, Brest, France
  • 3 Ifremer, NSE, Plouzané, France
  • 4 Kongsberg Discovery, Ocean Science, Horten, Norway

The final, formatted version of the article will be published soon.

    Detecting and locating emitted fluids in the water column is necessary for studying margins, identifying natural resources, and preventing geohazards. Fluids can be detected in the water column using multibeam echosounder data. However, manually analyzing the huge volume of this data for geoscientists is a very time-consuming task. Our study investigated the use of a YOLO-based deep learning supervised approach to automate the detection of fluids emitted from cold seeps (gaseous methane) and volcanic sites (liquid carbon dioxide). Several thousand annotated echograms collected from three different seas and oceans during distinct surveys were used to train and test the deep learning model. The results demonstrate first that this method surpasses current machine learning techniques, such as Haar-Local Binary Pattern Cascade. Additionally, we thoroughly analyzed the composition of the training dataset and evaluated the detection performance based on various training configurations. The tests were conducted on a dataset comprising hundreds of thousands of echograms i) acquired with three different multibeam echosounders (Kongsberg EM302 and EM122 and Reson Seabat 7150) and ii) characterized by variable water column noise conditions related to sounder artefacts and the presence of biomass (fishes, dolphins). Incorporating untargeted echoes (acoustic artefacts) in the training set (through hard negative mining) along with adding images without fluid-related echoes are the most efficient way to improve the performance of the model and reduce the false positives. Our fluid detector opens the door for near-real time acquisition and post-acquisition detection with efficiency, reliability and rapidity.

    Keywords: Multibeam echo sounder (MBES), water column data, Fluid detection, Automated processing, deep learning, YOLO (You Only Look Once), Underwater acoustic

    Received: 22 Nov 2024; Accepted: 31 Jan 2025.

    Copyright: © 2025 Perret, Le Chenadec, Gaillot, Ladroit and Dupré. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Tyméa Perret, Ifremer, Geo-Ocean, Plouzané, France

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.