Bayesian hindcast of acoustic transmission loss in the western P acific O cean

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.