Underwater Acoustic Research Trends with Machine Learning: Ocean Parameter Inversion Applications

• Main Authors: Haesang Yang, Keunhwa Lee, Youngmin Choo, Kookhyun Kim
• Format: Article
• Language: English
• Published: The Korean Society of Ocean Engineers 2020-10-01
• Series: 한국해양공학회지
• Subjects: underwater acoustics; sonar system; machine learning; deep learning; signal processing; parameter inversion abstract
• Online Access: https://www.joet.org/journal/view.php?number=2976

Underwater acoustics, which is the study of the phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. Underwater acoustics is mainly applied in the field of remote sensing, wherein information on a target object is acquired indirectly from acoustic data. Presently, machine learning, which has recently been applied successfully in a variety of research fields, is being utilized extensively in remote sensing to obtain and extract information. In the earlier parts of this work, we examined the research trends involving the machine learning techniques and theories that are mainly used in underwater acoustics, as well as their applications in active/passive SONAR systems (Yang et al., 2020a; Yang et al., 2020b; Yang et al., 2020c). As a follow-up, this paper reviews machine learning applications for the inversion of ocean parameters such as sound speed profiles and sediment geoacoustic parameters.