The University of Southampton
×
Filter your search:
University of Southampton Institutional Repository

Automatic detection of buried Mn-crust layers using a sub-bottom acoustic probe from AUV based surveys

Automatic detection of buried Mn-crust layers using a sub-bottom acoustic probe from AUV based surveys
Automatic detection of buried Mn-crust layers using a sub-bottom acoustic probe from AUV based surveys
A method for estimating the thickness of sediments covering buried layers of deep sea Cobalt-rich Manganese Crusts (Mn-crusts) from acoustic sub-bottom sonar data, recorded using an Autonomous Underwater Vehicle (AUV), is described. The acoustic data is analyzed with a combination of image and signal processing techniques to identify the optimal reflections coming from the seafloor and the buried layer. The method is applied to data collected from a field experiment and the results were validated using core samples from the same area; showing a high match. By including buried layers into volumetric estimation of Mn-crust deposits, resource potential of seafloor areas can be determined with better accuracy.
Neettiyath, Umesh
50a478b6-f18e-41b7-886d-11052eaa68b7
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Sugimatsu, Harumi
397df4fb-cbf7-4a12-abcf-df2245976a37
Sunaga, Takayuki
f29e306f-7d6e-4644-81e2-0629783167ea
Sakamoto, Junya
5def2b46-a7f8-41ef-a3c0-85595179ba8d
Hino, Hikari
0d6420b1-4b00-47ef-8b1e-3a5bd33d6934
Neettiyath, Umesh
50a478b6-f18e-41b7-886d-11052eaa68b7
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Sugimatsu, Harumi
397df4fb-cbf7-4a12-abcf-df2245976a37
Sunaga, Takayuki
f29e306f-7d6e-4644-81e2-0629783167ea
Sakamoto, Junya
5def2b46-a7f8-41ef-a3c0-85595179ba8d
Hino, Hikari
0d6420b1-4b00-47ef-8b1e-3a5bd33d6934

Neettiyath, Umesh, Thornton, Blair, Sugimatsu, Harumi, Sunaga, Takayuki, Sakamoto, Junya and Hino, Hikari (2022) Automatic detection of buried Mn-crust layers using a sub-bottom acoustic probe from AUV based surveys. Oceans 2022, India, Chennai, India. 21 - 24 Feb 2022. (doi:10.1109/OCEANSChennai45887.2022.9775260).

Record type: Conference or Workshop Item (Paper)

Abstract

A method for estimating the thickness of sediments covering buried layers of deep sea Cobalt-rich Manganese Crusts (Mn-crusts) from acoustic sub-bottom sonar data, recorded using an Autonomous Underwater Vehicle (AUV), is described. The acoustic data is analyzed with a combination of image and signal processing techniques to identify the optimal reflections coming from the seafloor and the buried layer. The method is applied to data collected from a field experiment and the results were validated using core samples from the same area; showing a high match. By including buried layers into volumetric estimation of Mn-crust deposits, resource potential of seafloor areas can be determined with better accuracy.

Text
Automatic_Detection_of_Buried_Mn-crust_Layers_Using_a_Sub-bottom_Acoustic_Probe_from_AUV_Based_Surveys - Version of Record
Restricted to Registered users only
Available under License Creative Commons Attribution No Derivatives.
Download (14MB)
Request a copy

More information

Published date: 28 February 2022
Additional Information: Funding Information: The data in this paper have been collected as a part of sponsored projects by Agency for Natural Resources and Energy, Ministry of Economy, Trade and Industry (METI), Japan, and Japan Oil, Gas and Metals National Corporation (JOGMEC). Part of this work was supported by the Japanese Ministry of Education under the Program for the Development of Fundamental Tools for the Utilization of Marine Resources. The authors also thank Dr. Kazunori Mizuno from the University of Tokyo for his help with sound velocity calculation. Publisher Copyright: © 2022 IEEE.
Venue - Dates: Oceans 2022, India, Chennai, India, 2022-02-21 - 2022-02-24
Related URLs:

Identifiers

Local EPrints ID: 457701
URI: http://eprints.soton.ac.uk/id/eprint/457701
DOI: doi:10.1109/OCEANSChennai45887.2022.9775260
PURE UUID: a442957e-918a-4dab-9206-9ef6eef7263b

Catalogue record

Date deposited: 16 Jun 2022 00:03
Last modified: 16 Mar 2024 17:47

Export record

Altmetrics

Contributors

Author: Umesh Neettiyath
Author: Blair Thornton
Author: Harumi Sugimatsu
Author: Takayuki Sunaga
Author: Junya Sakamoto
Author: Hikari Hino

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Library staff additional information
Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×