JOURNAL ARTICLE

Deep-water ambient sound over the Atlantis II seamounts in the Northwest Atlantica).

  • Published In: Journal of the Acoustical Society of America, 2024, v. 156, n. 4. P. 2687 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Walters, Matthew W.; Godin, Oleg A.; Joseph, John E.; Tan, Tsu Wei 3 of 3

Abstract

This article focuses on the spatial, temporal, and statistical variability of deep-water ambient underwater sound near the Atlantis II Seamounts in the northwest Atlantic, an area influenced by the Gulf Stream (GS). Using data from three Moored Autonomous acoustic Noise Recorders (MANRs) deployed at depths between 2573 and 4443 meters over 52 days, the study reveals that ambient sound intensity distributions deviate significantly from the commonly assumed normal (Gaussian) distribution, exhibiting heavier tails with high statistical significance. Ambient sound variability is influenced by bathymetric shadowing, nonuniform surface noise sources, wind speed, and the proximity of the GS, with sound intensity increasing by up to 10 dB when the GS axis is within 25 km of the receivers, likely due to GS currents enhancing surface wave breaking. Additionally, diurnal increases in sound intensity before sunrise and sunset suggest biological contributions, while spectral level variability decreases with frequency and differs from shallow-water sites. The findings highlight the complexity of ambient sound environments in dynamic ocean regions and underscore the need for further long-term studies to improve modeling and prediction of underwater acoustic conditions.

Additional Information

  • Source:Journal of the Acoustical Society of America. 2024/10, Vol. 156, Issue 4, p2687
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2024
  • ISSN:0001-4966
  • DOI:10.1121/10.0032360
  • Accession Number:180631980
  • Copyright Statement:Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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