Machine learning approach to prediction of real-time ocean sound speed profile

Abstract

Sound speed profile (SSP) determines sound propagation in the ocean. Variation of sound speed profiles with time and location heavily impacts underwater sonar systems used in ocean science and engineering. However, mapping sound speed profile over a vast area of the oceans in real-time is impossible by the traditional way, where sound speed is calculated from insitu profiling measurements of temperature, salinity, and pressure, which is expensive and, therefore, limited in sparse locations and time. Here, we aim to develop a machine learning model to predict real-time sound speed profiles anywhere in the global oceans. Our model takes advantage of the big data of long-term global temperature, salinity, and pressure measurements and further accounts for the variability of the surface ocean by inputting sea surface temperature and sea surface salinity data, which can be updated by satellite remote sensing in real-time. Here, SSPs are predicted for Pacific, Atlantic, and Indian ocean regions. The results show that the estimated SSPs had a root-mean-square error of 0.26 m/s and a coefficient of determination of 0.99. About 99% of the estimates lie within ±0.4 m/s of the SSPs obtained from in situ temperature and salinity profiles.