The main goal for our project is to further develop, implement, apply, and validate theory, algorithms, and computational schemes for dynamic environmental estimation, prediction, and acoustic inference (DEEP-AI). The specific research thrusts are to: (i) Predict and characterize underwater sound propagation PDFs due to the uncertain ocean oceanographic, bathymetry, and seabed fields, (ii) Assimilate the sparse acoustic and oceanographic data using multivariate principled Bayesian inversion and estimation of ocean oceanographic, acoustic, bathymetry, and seabed fields, (iii) Learn and discover acoustic parameterizations, model improvements, new processes, and most informative observation needs using new deep machine learning and Bayesian learning, and (iv) Develop efficient computational methods for the above prediction, assimilation, and learning.