Seafloor classification based on an AUV based sub-bottom acoustic probe data for Mn-crust survey

Neettiyath, Umesh, Sugimatsu, Harumi and Thornton, Blair (2024) Seafloor classification based on an AUV based sub-bottom acoustic probe data for Mn-crust survey. In OCEANS 2023 - MTS/IEEE U.S. Gulf Coast. IEEE. 5 pp . (doi:10.23919/OCEANS52994.2023.10504995).

Record type: Conference or Workshop Item (Paper)

Abstract

The possibility of automatically classifying high frequency sub-bottom acoustic reflections collected from an Autonomous Underwater Robot is investigated in this paper. In field surveys of Cobalt-rich Manganese Crusts (Mn-crusts), existing methods relies on visual confirmation of seafloor from images and thickness measurements using the sub-bottom probe. Using these visual classification results as ground truth, an autoencoder is trained to extract latent features from bundled acoustic reflections. A Support Vector Machine classifier is then trained to classify the latent space to idetify seafloor classes. Results from data collected from seafloor at 1500m deep regions of Mn-crust showed an accuracy of about 70%.

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