Predicting Deep Ocean Sound Speed by Stochastic Models.
Research rept. Apr 71-Oct 72,
NAVAL UNDERSEA CENTER SAN DIEGO CALIF
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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 msec 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
- Physical and Dynamic Oceanography
- Acoustic Detection and Detectors