ASSESSING OCEAN FLOOR MORPHOLOGY WITH AUVS

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Authors
Miner, Joshua M.
Subjects
autonomous underwater vehicle
AUV
bathymetry
Gaussian Process Model
position localization
sediment transport
stochastic map
Advisors
Horner, Douglas P.
Orescanin, Mara S.
Date of Issue
2024-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Sediment transport is an important characteristic of rivers and oceans, as one of the drivers of bathymetric change in coastal features and bottom topography. The flux of sediment can render this collected bathymetric data inaccurate over time, especially in the inner shelf and surf zone areas where change can occur on timescales of hours to days. This research introduces a novel framework for understanding sediment transport. It includes the use of an autonomous underwater vehicle (AUV) together with an onboard stochastic process for estimating bathymetry. The AUV permits greater access to hard-to-reach locations that is exemplified by the near-shore region. A stochastic process permits providing a complete map from sparse measurements together with confidence measures of the estimate. This can be used in two principal ways: first as part of an advanced AUV artificial intelligence/machine learning (AI/ML) technique and for real-time position estimation and second, over time, the maps can be compared for sediment transport analysis. This thesis shows the results of multiple AUV runs conducted over an 8-month period, offshore from Pajaro Beach near Monterey, CA, and the resultant sediment transport.
Type
Thesis
Description
Series/Report No
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NPS Report Number
Sponsors
Office of Naval Research
Funder
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Citation
Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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