Velocity and Attenuation Profiles in the Monterey Deep-Sea Fan

Abstract

Data obtained during a refraction experiment is used to estimate velocity and attenuation profiles in an area of thick sediments (2.5 -3 km). A vertical hydrophone array was deployed at mid-depth in 2800 meters of water. Estimates of velocity as a function of depth, and attenuation as a function of frequency and depth are obtained from an analysis of the pressure time series generated by the explosive charges and received at the array. To find the velocity profile, the sediment is modeled as a horizontally layered, laterally homogeneous medium. A least squares solution is found for the velocity gradients in each layer of the model. Velocity as a function of depth is obtained by integrating these gradients. A second method to infer velocity structure using linear programming takes upper and lower bounds on the input data and gives as a solution upper and lower bounds on the velocity profile. All velocity profiles consistent with the data lie within these bounds. Using a similar sediment model, a method of spectral ratios estimates attenuation in the sediment as a function of frequency and depth. Solving for each of the coefficients gives attenuation as a function of depth.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA193235

Entities

People

  • Richard K. Brienzo

Organizations

  • Scripps Institution of Oceanography

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Acoustic Properties
  • Acoustic Waves
  • Acoustics
  • Angle Of Arrival
  • Coefficients
  • Explosive Charges
  • Explosives
  • Frequency
  • Geography
  • Inverse Problems
  • Linear Programming
  • Materials
  • Oceanography
  • Oceans
  • Physical Properties
  • Refraction
  • Seabed

Readers

  • Acoustical Oceanography.
  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Calculus or Mathematical Analysis

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms