An overview of physics-based data science techniques in ocean acoustics

Scientific applications in ocean acoustics include inversion, signal detection, seabed classification, source localization, and environmental assessment. Traditionally, addressing these problems commonly rely on modeling, deterministic setups, or optimization methods. However, these solutions encounter challenges when faced with non-ideal data or experimental conditions, leading to ill-posed problems or assumptions that deviate from reality. Through a new approach in recent years the integration of data science and machine learning techniques has emerged as a promising solution across various research fields. Only recently have these methodologies been introduced to the field of ocean acoustics, offering efficient and alternative for addressing problems through data-driven approaches. This study presents a collection of techniques developed by the University of Delaware, focusing on acoustic and environmental assessment. The methodologies employed include statistical approaches like maximum entropy and data-driven methods, ranging from simpler strategies such as dictionary learning and image segmentation, to more sophisticated structures like convolutional neural networks and graph neural networks, the latter can exploit spatial information in the data. The results obtained from these diverse methods offer valuable insight, paving the way for innovative alternatives to physics-based problem-solving in ocean acoustics.