Bathymetry Estimation Using Machine Learning in the Ulleung Basin in the East Sea

• Published in: Sensors and materials Vol. 35; no. 9; p. 3351
• Main Authors: Kim, Kwang Bae, Kim, Ji Sung, Yun, Hong Sik
• Format: Journal Article
• Language: English
• Published: Tokyo MYU Scientific Publishing Division 01.01.2023
• Subjects: Altimeters; Bathymeters; Bathymetry; Estimation; Gravity anomalies; Machine learning; Satellite altimetry
• ISSN: 0914-4935; 2435-0869
• DOI: 10.18494/SAM4415

Accurate bathymetry estimation is made possible by combining depth data with free-air gravity anomalies on the sea surface recovered from the geoidal heights that are equivalent to the mean sea surface derived from satellite radar altimetry. The residual gravity anomalies that represent the short-wavelength effect are required to accurately estimate bathymetry by combining satellite altimetry-derived free-air gravity anomalies and shipborne data including depth and gravity anomalies. In this study, the optimized ensemble model of machine learning techniques was applied to the residual gravity anomalies to estimate bathymetry by the gravity–geologic method (GGM) from various geospatial information including shipborne depth, shipborne gravity anomalies, and satellite altimetry-derived free-air gravity anomalies, in the Ulleung Basin in the East Sea. From the results, the GGM bathymetry predicted using the optimized ensemble model of machine learning was improved by 32.3 m over the GGM bathymetry estimated using the original depth and gravity anomalies. The method presented in this study is for estimating deep-water bathymetry using machine learning, and it has been proven to have superior performance compared with conventional methods.