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Matched field processing with data-derived modes.

P Hursky1, W S Hodgkiss, W A Kuperman

  • 1Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, La Jolla, 92093, USA. paul.hursky@saic.com

The Journal of the Acoustical Society of America
|April 28, 2001
PubMed
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This study shows how to determine ocean acoustic properties and bottom characteristics using only sound data, eliminating the need for prior environmental knowledge. This method enhances underwater acoustic modeling and environmental characterization.

Area of Science:

  • Underwater acoustics
  • Oceanography
  • Signal processing

Background:

  • Traditional underwater acoustic modeling often requires extensive prior knowledge of the ocean environment, including bottom properties.
  • Estimating acoustic modes directly from vertical line array data presents challenges due to limited frequency and depth sampling.

Purpose of the Study:

  • To develop a method for deriving complete acoustic mode information and environmental parameters directly from experimental data.
  • To demonstrate the feasibility of matched field processing (MFP) without a priori environmental information.

Main Methods:

  • Utilizing data-derived modes and measured sound speed profiles to infer wave numbers and bottom parameters.
  • Employing vertical line array data to estimate mode shapes without numerical wave field models.

Related Experiment Videos

  • Validating the derived parameters and MFP performance using data from the SWellEx-96 experiment.

    • Main Results:

    • Successfully derived self-consistent sets of modes, wave numbers, and bottom parameters from experimental data.
    • Demonstrated the ability to calculate modes across all frequencies, not just those initially captured.
    • Validated the derived acoustic model for MFP on independent data tracks.

    Conclusions:

    • The proposed method effectively characterizes the underwater acoustic environment and bottom properties using only acoustic measurements.
    • This approach offers a robust alternative to traditional MFP methods that rely on a priori environmental data.
    • The findings have significant implications for improving underwater acoustic propagation modeling and source localization.