NRL-APL Grain Size Algorithm Upgrade

Abstract

This upgrade was designed to improve upon older algorithms used to infer input geoacoustic parameters fro high-frequency acoustic models from sediment grain size. These older algorithms were based on limited data set and were developed by adjusting acoustic model-data fits rather than by statistical regression. The upgrade has two components. The regression analysis of sediment grain size and geoacoustic properties was performed by NRL and the determination of accuracy of acoustic backscatter perditions was performed by APL-UW. The geoacoustic properties/sediment grain size relationships produced a new algorithm connecting acoustic model parameters with the parameters of the MIW (Mine Warfare) sediment database.

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Document Details

Document Type
Technical Report
Publication Date
Jun 28, 2002
Accession Number
ADA403759

Entities

People

  • Darrell R. Jackson
  • K. Y. Moravan
  • Kevin B. Briggs

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Acoustic Impedance
  • Acoustic Measurement
  • Acoustic Properties
  • Algorithms
  • Backscattering
  • Continental Shelves
  • Data Sets
  • Databases
  • Grain Size
  • Grazing Angles
  • Military Research
  • Physical Properties
  • Regression Analysis
  • Scattering
  • Seabed
  • Shallow Water

Readers

  • Acoustical Oceanography.
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks