Seabed classification and source localization from ship spectrograms using deep learning models

An 18-layer residual neural network (ResNet-18) and a convolutional neural network (CNN) are used for source localization and seabed classification using broadband spectrograms obtained from ship-radiated noise. The propagation of the broadband noise from those ships of opportunity (SOO) contains significant information about the waveguide and seafloor. In this work, a ResNet-18 and a CNN are trained using multi-task learning to estimate the closest point of approach, ship speed, and seabed type. Training data are simulated using a range-independent normal modes model and several sediment profiles obtained by inversions in different regions. The input data samples correspond to 20 min long broadband spectrograms in the 300–1500 Hz band. During the training stage, data augmentation techniques are also applied to evaluate their effects on the predictions. The metrics used to evaluate performance are accuracy for seabed classification and root mean square error for source localization. Results demonstrate the ability of deep learning models to estimate source localization and seabed classification along with the impact of data augmentation techniques using SOO spectrograms. [Work supported by ONR, Contract No. N00014-19-C-2001.]