Predicting Deep Ocean Sound Speed by Stochastic Models.

Abstract

Archived hydrographic cast data were the basis of a stepwise regression analysis to develop a stochastic model of sound-speed distribution below a depth of 500 m in the Gulf of Alaska. A simple polynomial expression in three variables was found to fit the empirical data with a standard deviation of 0.51 m/sec and a percent variance explained by regression of 98.6 percent. Various comparisons of sound predictions (from the model) with observed data indicated close agreement. The accuracy and compactness of the model make it ideally suited for various applications in sonar prediction, as well as for rapid information retrieval. The method is recommended for employment in other geographical areas of interest to the Navy. (Author)

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1973
Accession Number
AD0758735

Entities

People

  • H. W. Frye

Organizations

  • Naval Undersea Warfare Center

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Agreements
  • Computing-Related Activities
  • Data Science
  • Deep Oceans
  • Employment
  • Information Retrieval
  • Information Science
  • Mathematics
  • Oceans
  • Polynomials
  • Regression Analysis
  • Standards

Readers

  • Computational Modeling and Simulation
  • Regression Analysis.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

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