An Iterative Algorithm for Predicting Seafloor Topography from Satellite Altimetry Gravity Data

As high-resolution global coverage cannot easily be achieved by direct bathymetry, the use of gravity data is an alternative method to predict seafloor topography. Currently, the commonly used algorithms for predicting seafloor topography are mainly based on the approximate linear relationship between topography and gravity data. In actual application, it is also necessary to process the corresponding data according to some empirical methods, which can cause uncertainty in predicting topography. We established analytical observation equations between the gravity data and topography, and obtained the corresponding iterative solving method based on the least square method after linearizing the equations. Furthermore, the regularization method and piecewise bilinear interpolation function are introduced into the observation equations to effectively suppress the high-frequency effect of the boundary sea region and the low-frequency effect of the far sea region. Finally, the seafloor topography beneath two sea region (117.25-118.25 E, 13.85-14.85 N), (112.6-113.25 E,11.0-11.65 N) in the South China Sea are predicted as an actual application, where gravity data of the study area with a resolution of 11 is from the DTU17 model. Comparing the prediction results with the data of ship soundings from the National Geophysical Data Center (NGDC), the root-mean-square (RMS) error and relative error can be up to 127.4 m(gravity anomaly) and 109m(vertical gravity gradient anomaly). The bathymetry predicted from gravity could be combined with ship soundings for the next generation of bathymetry map.