Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea

Abstract

This study aims to improve the accuracy of bathymetry predicted by gravity-geologic method (GGM) using the optimal machine learning model selected from machine learning techniques. In this study, several machine learning techniques were utilized to determine the optimal model from the performance of depth and gravity anomalies. In addition, a tuning density contrast calculated from satellite altimetry-derived free-air gravity anomalies (FAGAs) was applied to estimate enhanced bathymetry. By comparison with shipborne depth, the accuracy of the bathymetry estimated by using satellite altimetry-derived FAGAs and machine learning was evaluated. The findings reveal that the bathymetry predicted by the optimal machine learning using the Gaussian process regression and the GGM with a tuning density contrast can enhance the accuracy of 82.64 m, showing an improvement of 67.40% in the RMSE at shipborne depth measurements. Although the tuning density is larger than 1.67 g/cm3, bathymetry using satellite altimetry-derived FAGAs and machine learning can be effectively improved with higher accuracy.

Keywords

• optimal machine learning
• gravity anomalies
• density contrast
• east sea