  In this work model-based methods are employed along with machine learning
techniques to classify sediments in oceanic environments based on the
geoacoustic properties of a two-layer seabed. Two different scenarios are
investigated. First, a simple low-frequency case is set up, where the acoustic
field is modeled with normal modes. Four different hypotheses are made for
seafloor sediment possibilities and these are explored using both various
machine learning techniques and a simple matched-field approach. For most noise
levels, the latter has an inferior performance to the machine learning methods.
Second, the high-frequency model of the scattering from a rough, two-layer
seafloor is considered. Again, four different sediment possibilities are
classified with machine learning. For higher accuracy, 1D Convolutional Neural
Networks (CNNs) are employed. In both cases we see that the machine learning
methods, both in simple and more complex formulations, lead to effective
sediment characterization. Our results assess the robustness to noise and model
misspecification of different classifiers.
