An Adaptive Nonlinear Iterative Method for Predicting Seafloor Topography From Altimetry‐Derived Gravity Data

Abstract

The ocean covers 71% of the Earth's surface. At present, only about 20% of the seafloor topography (ST) has been directly measured by ships, and most areas are predicted from satellite altimetry‐derived gravity products. In this study, an adaptive nonlinear iterative (ANI) method is proposed to address two major problems in gravity ST inversion: linear approximation and empirical seafloor density contrast (SDC). In ANI, the SDC is adaptively estimated as an output, while higher‐order Parker expansion and modified Bott's iteration are combined to recover nonlinear topography. We apply our new method using the DTU21GRA altimetric gravity model and single‐beam bathymetry to predict the ST in a part of the South China Sea. Results reveal that the average SDC in the study area is 1.24 g/cm ³, which compares well to CRUST1.0. The root‐mean‐square (RMS) error between the nonlinear model and single‐beam checkpoints is 102.1 m, which is improved by 34.5%, 29.2%, and 18.3% compared with the non‐gravity model, topo_24.1, and linear model, respectively. The RMS error between the nonlinear model and multibeam bathymetry is 91.0 m, which is better than the linear model. Analysis of two‐dimensional profiles shows that the nonlinear model reveals more terrain details than the linear model.

Plain Language Summary

Seafloor bathymetry has important significance for understanding ocean tectonic evolution and ocean circulation. Bathymetric mapping with shipborne sonar instruments is feasible but expensive and time‐consuming. After decades of efforts, only about 20% of the seafloor topography (ST) is directly mapped, and the remaining is predicted by satellite altimetry‐derived gravity products. This study uses the latest gravity model and proposes a new method to refine the ST in a part of the South China Sea. The new method has several advantages. First, it does not require preset an empirical density parameter. Second, it is based on a more rigorous mathematical relationship between seafloor bathymetry and gravity. Using this method, we find that the density contrast between seawater and topography is far less than the theoretical value; the ST estimated by the new method can reveal more detailed terrain and improve accuracy, especially in rugged areas.

Key Points

• Seafloor topography (ST) and density contrast are simultaneously predicted by using altimetry‐derived gravity data and ship soundings

• Higher‐order Parker expansion and iteration are applied to model the nonlinearities between gravity and bathymetry

• A refined ST model of the South China Sea has been constructed to reveal more terrain details