Machine Learning-Driven Lateral Density Variation for High-Precision Bathymetry: Central-Northern South China Sea

Abstract

Uniform density-contrast assumptions in gravity-derived bathymetry produce substantial systematic errors. This problem stands out in regions with strong lateral density variation, such as the central–northern South China Sea. Conventional constant or simple vertically varying density models fail to capture these complexities. To overcome this limitation, a spatially varying density-contrast field is constructed by integrating multi-source geophysical data (crustal, gravity, and bathymetric data) using a back-propagation (BP) neural network. This field is incorporated into an adaptive Parker–based inversion, yielding a high-resolution bathymetric grid with Root Mean Square (RMS) improvements of 2.6 m over the constant-density approach, together with the smallest systematic bias. The most significant gains occur in shallow reef-dominated waters (0 to −1500 m), where relative RMS reductions reach approximately 9%. By coupling neural network-derived density modelling with physically rigorous inversion, the approach overcomes limitations of uniform-density assumptions while retaining interpretability, providing an efficient and reliable approach to high-precision seafloor mapping in geologically complex regions.