Bathymetry Estimation Using Machine Learning and Tuning Density Contrast in the Yamato Basin of the East Sea (Sea of Japan)

The purpose of this study is to evaluate the accuracy of bathymetry predicted by the gravity-geologic method (GGM) using depth and gravity anomalies estimated using the optimized machine learning model determined from machine learning techniques around the Yamato basin in the East Sea. In this study, the optimized model using machine learning techniques was applied to estimate bathymetry using gravity-geologic method (GGM) from a tuning density contrast between seawater and the seafloor bedrock determined by downward continuation method using satellite altimetry-derived gravity anomalies. Bathymetry estimated using machine learning technique was assessed the accuracy in comparison with shipborne depth measurement obtained by the National Centers for Environmental Information (NCEI, https://www.ncei.noaa.gov), the National Oceanic and Atmospheric Administration (NOAA, http://www.noaa.gov). As a result, the GGM bathymetry predicted by the optimized machine learning model using a tuning density contrast of 13.63 g/cm³ in comparison with that using a density contrast of 1.67 g/cm³ shows the improvement of 67.40% in the RMSE at shipborne locations of the NECI.