Bayesian hindcast of acoustic transmission loss in the western Pacific Ocean
Abstract
A Bayesian network is developed to demonstrate the feasibility of using environmental acoustic feature vectors (EAFVs) to predict underwater acoustic transmission loss (TL) versus range at two locations for a single acoustic source depth and frequency. Features for the networks are chosen based on a sensitivity analysis. The final network design resulted in a well‐trained network, with high skill, little gain error, and low bias. The capability presented here shows promise for expansion to a more generalized approach, which could be applied at varying locations, depths and frequencies to estimate acoustic performance over a highly variable oceanographic area in real‐time or near‐real‐time.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2016
- Source ID
- 10.1002/2016jc011982
Entities
People
- J. Paquin Fabre
- Margaret L. Palmsten
Organizations
- Office of Naval Research
- United States Naval Research Laboratory