Ensemble approach to deep learning seabed classification using multichannel ship noise

In shallow-water downward-refracting ocean environments, hydrophone measurements of shipping noise encode information about the seabed. In this study, neural networks are trained on synthetic data to predict seabed classes from multichannel hydrophone spectrograms of shipping noise. Specifically, ResNet-18 networks are trained on different combinations of synthetic inputs from one, two, four, and eight channels. The trained networks are then applied to measured ship spectrograms from the Seabed Characterization Experiment 2017 (SBCEX 2017) to obtain an effective seabed class for the area. Data preprocessing techniques and ensemble modeling are leveraged to improve performance over previous studies. The results showcase the predictive capability of the trained networks; the seabed predictions from the measured ship spectrograms tend towards two seabed classes that share similarities in the upper few meters of sediment and are consistent with geoacoustic inversion results from SBCEX 2017. This work also demonstrates how ensemble modeling yields a measure of precision and confidence in the predicted results. Furthermore, the impact of using data from multiple hydrophone channels is quantified. While the water sound speed in this experiment was only slightly upward refracting, we anticipate increased advantages of using multiple channels to train neural networks for more varied sound speed profiles.