Comparison of maximum entropy and ResNet-18 inferences of sediment sound speed using surface ship noise

Abstract

The impact of individual seabed properties on sound propagation in the ocean depends on many factors including source-receiver range and frequency band of interest. In this talk, estimates of the sound speed ratio across the water-sediment interface are obtained using a maximum entropy approach and ResNet18, a supervised machine learning model. The input data are spectrograms of surface ship noise from shipping lanes. Synthetic spectrograms are modeled using a ship noise source spectrum and a range-independent normal mode model, ORCA, with a wide range of environments and ship parameters. Experimental data from the New England Mud Patch are used with both inverse methods. The maximum entropy approach uses data-model mismatch to obtain a posterior probability distribution for the parameters of interest. The ResNet18 is trained on the synthetic spectrograms, augmented with additive noise, and then applied to the experimental data. A comparison of the results from these two methods for a variety of ships using different frequency bands will be presented, along with a discussion of the advantages and limitations of each method.