Semi-supervised graph learning for underwater source localization using ship-of-opportunity spectrograms

Conventional techniques for underwater source localization have traditionally relied on optimization methods, matched-field processing, beamforming, and, more recently, deep learning. However, these methods often fall short to fully exploit the data correlation crucial for accurate source localization. This correlation can be effectively captured using graphs, which consider the spatial relationship among data points through edges. This work introduces a novel graph learning module for source localization using spectrograms from ships-of-opportunity, which represent mid-frequency acoustic broadband signals from ship-radiated noise ranging from 360 to 1100 Hz, collected during the 2017 Seabed Characterization Experiment (SBCEX 2017). The proposed approach follows a two-step process: first, a pre-trained convolutional neural network (CNN) module is used for feature extraction via self-supervised learning, and then a graph neural network model is trained using semi-supervised learning for source localization. The graph is constructed using a k-nearest neighbor algorithm, incorporating features extracted by the CNN from the spectrograms. By employing this two-stage training strategy, our framework addresses the challenge of limited labeled data availability while achieving performance comparable to conventional supervised learning models. The effectiveness of our approach is demonstrated through model evaluation on both synthetic and measured data, showcasing the architecture's ability to generalize well to unseen scenarios.