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Bayesian hindcast of acoustic transmission loss in the western P acific O cean
Author(s) -
Palmsten Margaret,
Paquin Fabre J.
Publication year - 2016
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1002/2016jc011982
Subject(s) - transmission (telecommunications) , transmission loss , hindcast , bayesian probability , range (aeronautics) , computer science , underwater , sensitivity (control systems) , feature (linguistics) , variable (mathematics) , acoustics , geology , telecommunications , engineering , artificial intelligence , machine learning , mathematics , electronic engineering , physics , oceanography , mathematical analysis , linguistics , philosophy , aerospace engineering
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.