Deep-learning source localization using autocorrelation functions from a single hydrophone in deep ocean

Abstract

In the direct arrival zone of the deep ocean, the multi-path time delays have been used for acoustic source localization. One of the challenges in conventional localization methods is to artificially determine which paths the extracted delays belong to. A convolutional neural network, taking the autocorrelation functions as the input feature directly, is proposed for source localization to avoid the path determination procedure. Since some multi-path arrivals may not be visible due to absorption in the bottom of the ocean, a data augmentation method based on a ray propagation model is proposed. Tests on simulated and real data validate the method.