Inferring seafloor topography from gravity anomaly currently is the dominant method to obtain a global view of the oceans. Standard techniques rely on an approximate, linear relationship between topography and gravity, which is valid only if the local topography is smooth compared with the regional topography, so the estimation accuracy in the very rugged areas is low. Current methods can be improved by removing the linear approximation and estimating the topography through simulated annealing and by using gravity gradiometry that is more sensitive to topography at short wavelengths than the gravity anomaly. Simulated annealing is a global optimization technique that can process nonlinear inverse problems. It is developed to estimate the seafloor depths by minimizing the difference between the observed and forward‐computed vertical gravity gradients. The method is tested on altimetry‐derived gravity gradients in a 2^∘×2^∘ area of rugged seafloor topography in the West Pacific Ocean and results in estimates with a root‐mean‐square error of ±236 m. Compared to estimates from an existing model obtained by standard techniques this represents an accuracy improvement of 22%.

Currently, seafloor topography inversion based on satellite altimetry gravity data provides the principal means to predict the global seafloor topography. Researchers often use sea surface geoid height or gravity anomaly to predict sea depth in the space domain. In this paper, a comprehensive discussion on seafloor topography inversion formulas in the space domain is presented using sea surface geoid height, gravity anomaly and introduces an approach that uses vertical gravity gradient. This would be the first study to estimate seafloor topography by vertical gravity gradient in the space domain. Further, a nonlinear iterative least-square inversion process is discussed. Using the search area for the Malaysia Airlines Flight MH370 as study site, we used the DTU17 gravity anomaly model and SIO V29.1 vertical gravity gradient to generate the seafloor topography. The results of the proposed bathymetric models were analyzed and compared with the DTU18 and SIO V20.1 bathymetric models. The experimental results show that the gravity anomaly and vertical gravity gradient in the study area are strongly correlated with the seafloor topography in the 20–200 km wavelength range. The optimal initial iteration values for seafloor topography variance and correlation length are 0.6365 ${\mathrm{km}}^2$ and 10.5′, respectively. Shipborne measurements from SONAR data were used as external checkpoints to evaluate the bathymetric models. The results show that the RMS for BAT_VGG_ILS (inversion model constructed by vertical gravity gradient) is smaller than for BAT_GA_ILS (inversion model constructed by gravity anomaly) and BAT_GA_VGG_ILS (inversion model constructed by gravity anomaly and vertical gravity gradient). The relative accuracy of the DTU18 bathymetry model was 9.27%, while the relative accuracy of the proposed seafloor models was higher than 4%. Within the 200 m difference range, the proportion of checkpoints for BAT_VGG_ILS was close to 95%, about 80% for BAT_GA_ILS and BAT_GA_VGG_ILS, and less than 50% for the DTU18. The results show that the nonlinear iterative least square method in the space domain is feasible.

The bathymetry under the Amery Ice Shelf steers the flow of ocean currents transporting ocean heat, and thus is a prerequisite for precise modeling of ice‐ocean interactions. However, hampered by thick ice, direct observations of sub‐ice‐shelf bathymetry are rare, limiting our ability to quantify the evolution of this sector and its future contribution to global mean sea level rise. We estimated the bathymetry of this region from airborne gravity anomaly using simulated annealing. Unlike the current model which shows a comparatively flat seafloor beneath the calving front, our estimation results reveal a 255‐m‐deep shoal at the western side and a 1,050‐m‐deep trough at the eastern side, which are important topographic features controlling the ocean heat transport into the sub‐ice cavity. The new model also reveals previously unknown depressions and sills that are critical to an improved modeling of the sub‐ice‐shelf ocean circulation and induced basal melting.

We introduce an iterative inversion method to address the problems in high-order seafloor topography inversion using gravity data (gravity anomaly and vertical gravity gradient anomaly), such as the difficulty in computing the equation and the uniqueness of the calculation results. A part of the South China Sea is selected as the experimental area. Considering the coherence and admittance function of gravity topography and vertical gravity gradient topography, the inversion band of the gravity anomaly and vertical gravity gradient anomaly in the study area is 30 km–120 km. Seafloor topography models of different orders are constructed using an iterative method, and the performance of each seafloor topography model is analyzed against ETOPO1 and other seafloor topography models. The experimental results show that as the inversion order increases, the clarity and richness of seafloor topographic expression continuously improve. However, the accuracy of seafloor topography inversion does not improve significantly when the inversion order exceeds a certain value, which is related to the contribution of high-order seafloor topography to gravity information. The results show that the accuracy of BGT4 (inversion model constructed by the gravity anomaly) is slightly poorer than that of BVGGT4 (inversion model constructed by the vertical gravity gradient anomaly) in areas with complex topography, such as multi-seamounts and trenches, and the results are generally better in areas with flat seafloor topography.

Seafloor topography shapes the pathways of ocean currents transporting ocean heat and, thus, is a fundamental boundary condition for modeling ocean‐ice interactions. However, few ship bathymetric data are available on the inner continental shelf of northeast Greenland due to the year‐round presence of sea ice. We infer seafloor topography of this region from airborne gravity anomaly measured by National Aeronautics and Space Administration's Oceans Melting Greenland (OMG) mission through a nonlinear inversion method called simulated annealing and results in a model with 1.95–3.9 km resolution and 52 m accuracy. The model provides a view of the seafloor near Zachariæ Isstrøm and reveals previously unknown topographic features such as a 370–560 m deep trough that enables warm subsurface water to reach Jøkelbugten fjord into which Zachariæ Isstrøm drains. Present bathymetric models do not show deep enough troughs near Jøkelbugten fjord allowing the inflow of warm water.

Compared with airborne gravimetry, a technique frequently used to infer the seafloor topography at places inaccessible to ship soundings due to the presence of ice shelf or ice mélange, airborne gravity gradiometry inherently could achieve higher spatial resolution, thus it is promising for improved inference of seafloor topography. However, its estimation capability has not been demonstrated by real projects. Theoretical analysis through admittance shows that compared with gravity disturbance, gravity gradient is more sensitive to the short-wavelength seafloor topography but diminishes faster with the increase of the distance between the seafloor and airplane, indicating its superiority is recovering short-wavelength topographic features over shallow waters. We present the first numerical experiment that estimates seafloor topography from a 0.4-km resolution, real airborne gravity gradients. It is shown that airborne gravity gradiometry can recover smaller topographic features than typical airborne gravimetry, but the estimation accuracy is only $\pm 17$ m due to the presence of subsurface density variations. The long-wavelength effect of the subsurface density variations can be removed with the aid of constraining bathymetry inside the study area, whereas the short wavelengths cannot. This study expands the applications of airborne gravity gradiometry, and helps glaciologists understand its performance in seafloor topography estimation.