Seafloor topography of Huangyan seamount chain based on GA-BP algorithm from ship survey and gravity anomaly data

Abstract

Seabed topography is important for marine geophysics and geodesy. However, conventional seabed topography inversion methods based on gravity data are constrained to a linear fitting, neglecting the impact of nonlinear terms. In this paper, we propose an innovative method for seabed topography inversion by establishing fresh mathematical relationships through machine learning techniques based on the Smith and Sandwell (SAS) method. Utilizing global sea depth data from the National Geophysical Data Center (NGDC) and gravity anomaly data from satellite altimetry, our study employs the SAS method for seabed topography inversion. The improved SAS method, enhanced by the Genetic Algorithm-Backpropagation (GA-BP) algorithm, is specifically applied to invert the Huangyan Seamount Chain topography in the South China Sea. The accuracy is evaluated by seabed topography models, including EOTPO1, GEBCO-2023, and S & S V19.1. The results show the GA-BP method significantly reduces residuals and improves the accuracy estimated by the SAS inversion method. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are decreased by 14.98% and 5.07%, respectively. The proposed method has valuable reference significance for future marine exploration endeavours around the world.