A deep sea sound velocity profile inversion method combining World Ocean Atlas 2023 data and real-time surface sound velocity

To address the challenge of obtaining real-time full-depth sound velocity profile (SVP) in deep-sea bathymetric surveys using unmanned multibeam systems, a method is proposed that combines World Ocean Atlas 2023 (WOA23) temperature and salinity model data with multibeam surface sound speed to invert real-time full-depth SVPs. Firstly, empirical orthogonal function (EOF) analysis is conducted on the SVPs calculated from the WOA23 temperature and salinity model data in the survey area to obtain spatial functions and temporal coefficients. Then, the temporal coefficients are used as candidate solution variables for the alpha evolution algorithm, and the difference between the surface sound speed measured by the multibeam bathymetric system and the sound speed at the same depth of the inverted SVP is used to construct the fitness function for iterative inversion of the SVP. Finally, the change trend of the field-measured SVP is considered, and the inverted SVP is combined with the SVP obtained through spatial interpolation to yield the final inverted SVP. The analysis of a case study indicates that compared with the SVP commonly used by surveyors as a substitute for nearby sound speed, the maximum error, mean absolute error (MAE), and root mean square error (RMSE) of the inverted SVP are reduced by 46.65%, 39.04%, and 45.23%, respectively. After sound speed correction, the maximum error, MAE, and RMSE of the bathymetric data are reduced by 76.34%, 75.36%, and 75.42%, respectively. This method effectively mitigates the impact of the representativeness error of the SVP, enhancing the efficiency and data quality of deep-sea unmanned multibeam bathymetric surveys.