Seabed Classification Using a Convolutional Neural Network on Explosive Sounds

Abstract

The Seabed Characterization Experiment 2017 yielded a rich set of environmental and acoustical data, and subsequent geoacoustic inversions have estimated seabed properties. Seventeen of these seabed parameterizations are now used to define seabed classes, and a convolutional neural network (CNN) is trained to select a seabed class using explosive sounds. The CNN is trained on synthetic 1-s pressure time series and then applied to measured data samples from a single pressure sensor. While the environmental variability in the training data impacts the seabed classification, physical insights are gained by considering the classification results as a function of the sound speed ratio across the sediment–water interface and the interval velocity of the top sediment layer. These results indicate that the selected seabed classes for the data samples with longer propagation distances consistently have similar sediment–water sound speed ratios that are less than unity, while the data samples with the shortest propagation distances consistently have higher interval velocities. These classification results indicate that the CNN has learned physical features associated with acoustic sound propagation and points to future work that needs to be considered if the seabed classes have acoustically distinct signatures in the data.