Seabed classification from ship-radiated noise using an ensemble deep learning

Abstract

An ensemble of deep learning algorithms is employed for seabed classification using merchant ship-radiated noise recorded on a single receiver. Five different convolutional neural network (CNN) architectures and one residual neural network (ResNet) are trained on synthetic data generated using 34 seabed types found in the literature from different areas of the world that span from soft-muddy to hard-sandy sediments. Validation results show an accuracy above 97.15% for all the networks using the five-fold cross-validation method. To assess the generalizability performance of the ensemble deep learning, the networks were tested on 69 data samples measured on three VLAs in the Seabed Characterization Experiment in 2017 (SBCEX 2017) from merchant ships passing by close to the receivers. Results show that the most likely sediments were the mud over sand environments inferred in previous geoacoustic inversions for the SBCEX 2017 area with 94% of the predictions from all the trained networks. This work presents evidence that the ensemble of deep learning algorithms has learned how the signature of the sediments is encoded in the ship-radiated noise providing a unified classification result when tested on real-world data. [Work supported by ONR, Contract No. N00014-19-C-2001.]