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arXiv · 2309.14861

Seafloor Classification based on an AUV Based Sub-bottom Acoustic Probe Data for Mn-crust survey

Umesh Neettiyath·Harumi Sugimatsu·Blair Thornton
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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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Umesh Neettiyath, Harumi Sugimatsu, Blair Thornton. 2023-09-26. Seafloor Classification based on an AUV Based Sub-bottom Acoustic Probe Data for Mn-crust survey. https://arxiv.org/abs/2309.14861

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