EXPERIMENTAL INVESTIGATION OF THE VARIABILITY AND CORRELATION PROPERTIES OF NEAR-BOTTOM AMBIENT SOUND IN A DEEP OCEAN
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Authors
Walters, Matthew W.
Subjects
ambient sound
deep ocean
acoustic noise interferometry
acoustic remote sensing
New England Seamounts
sonar performance prediction
sound in horizontally inhomogeneous ocean
deep ocean
acoustic noise interferometry
acoustic remote sensing
New England Seamounts
sonar performance prediction
sound in horizontally inhomogeneous ocean
Advisors
Fargues, Monique P.
Joseph, John E.
Godin, Oleg A.
Olson, Derek
Gemba, Kay L.
Date of Issue
2024-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
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Abstract
Design and operation of deep-water ocean observing systems as well as acoustic monitoring of undersea infrastructure require an improved understanding of the variability and environmental information content of near-bottom ambient sound. Reliable sonar performance predictions in deep water over complex bathymetry are impossible without a realistic representation of ambient noise in such areas. This work aims to improve physical understanding of deep-water ambient sound by acquiring and analyzing synchronized, long-term, spatially distributed time series of noise using a network of deep-water Moored Autonomous acoustic Noise Receivers (MANRs). The receivers are deployed in an area of high oceanographic interest, where the Gulf Stream interacts with the New England Seamounts. The data reveal the spectral and statistical properties of ambient sound and allow to characterize its spatial and temporal variability. Notably, it is established with high statistical significance that the data is incompatible with the common assumption of normally distributed noise in deep water. Ambient sound observations help to characterize the Gulf Stream influence on the soundscape and infer the speed of near-bottom currents using both Neural Network and Regression Tree methods. Acoustic noise interferometry techniques are applied to characterize the water column variability in the area containing the MANR network.
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Thesis
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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.