Ship-Induced Noise Predictions in the Atlantic and the Pacific: A Comparison of Two Noise Models

Abstract

This report describes differences in the ship-induced, bearing-elevation, noise directionality obtained from different noise models and interprets those differences in terms of the propagation and the environmental components of those models. The two noise models, RANDI and APL, both compute the noise as the incoherent sum of the individual ship contributions; they differ in both their propagation models (ANM and FEPE) and in their environmental models. The directionalities are computed for both the Sargasso Sea and the Gulf of Alaska. The Sargasso Sea results differ significantly with the APL directionality, showing lower levels and a much deeper noise notch. In the Gulf of Alaska, the directionalities are much more similar, with both exhibiting deeper noise notches than in the Sargasso Sea. The disparity in the Sargasso Sea noise notch results primarily because, for the ANM model, the set of modes excited by sources near the continental shelf is disjoint from the modes observed by the array. For the Gulf of Alaska, this "mode-set-disjunction" does not occur, and hence, the ANM more realistically estimates the downslope propagating contributions.

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Document Details

Document Type
Technical Report
Publication Date
May 30, 2006
Accession Number
ADA473782

Entities

People

  • Iman W. Schurman
  • Lisa A. Pflug
  • Richard Heitmeyer
  • Stephen C. Wales
  • Thomas J. Hayward

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acoustic Fields
  • Acoustic Propagation
  • Computations
  • Continental Shelves
  • Continental Slopes
  • Convergence Zones (Sonar)
  • Deep Water
  • Grazing Angles
  • Grids
  • Low Angles
  • Military Research
  • Noise
  • Physics
  • Physics Laboratories
  • Reflection
  • Sargasso Sea
  • Ship Noise

Readers

  • Acoustical Oceanography.
  • Acoustics.
  • Wave Propagation and Nonlinear Chaotic Dynamics.