Predicting Acoustic Transmission Loss Uncertainty in Ocean Environments with Neural Networks

Computational predictions of acoustic transmission loss (TL) in ocean environments depend on the relevant environmental characteristics, such as the sound speed field, bathymetry, and seabed properties. When databases are used to obtain estimates of these properties, the resulting predictions of TL are uncertain, and this uncertainty can be quantified via the probability density function (PDF) of TL. A machine learning technique for quickly estimating the PDF of TL using only a single, baseline TL calculation is presented here. The technique shifts the computational burden from present-time Monte-Carlo (MC) TL simulations in the environment of interest to ahead-of-time training of a neural network using equivalent MC TL simulations in hundreds of ocean environments. An environmental uncertainty approach which draws information from global databases is also described and is used to create hundreds of thousands of TL-field examples across 300 unique ocean environments at ranges up to 100 km for source frequencies between 50 and 600 Hz. A subset of the total dataset is used to train and compare neural networks with various architectures and TL-PDF-generation methods. Finally, the remaining dataset examples are used to compare the machine-learning technique's accuracy and computational effort to that of prior TL-uncertainty-estimation techniques.

Automated summary

This paper presents a machine learning technique for quickly estimating the probability density function (PDF) of acoustic transmission loss (TL) in ocean environments. The technique shifts the computational burden by training a neural network using equivalent Monte-Carlo (MC) TL simulations in hundreds of ocean environments. Experimental results demonstrate that the proposed method is more accurate and computationally efficient compared to prior TL-uncertainty-estimation techniques.