A convolutional neural network for source range and ocean seabed classification using pressure time-series

Neural networks learn features that are useful for classification directly from a source, such as a recorded signal, which removes the need for feature extraction or domain transformations necessary in other machine learning algorithms. To take advantage of these benefits and have a finer temporal resolution, a one-dimensional convolutional neural network is applied to pressure time-series to find source range and ocean environment class from a received signal. The neural network was trained on simulated signals generated in different environments (sandy, muddy, or mixed-layer sediment layers) for several ranges (0.5 to 15 km). We found significant potential in a neural network of this type, given a large amount of varied training samples for the network, to learn important features suitable for range and environment predictions. This type of network provides an alternative for frequency-domain learning and is potentially useful for impulsive sources.