Seafloor Topography Estimation from Gravity Anomaly and Vertical Gravity Gradient Using Nonlinear Iterative Least Square Method

Abstract

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 km2 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.

An iterative algorithm for predicting seafloor topography from gravity anomalies

“…Variations of the gravity anomaly on the sea surface are mainly caused by the mass deficit by the seafloor topography, density anomaly of the lithosphere, and isostatic compensation of mass below the lithosphere. The mass deficit by seafloor topography significantly contributes to the gravity anomaly on the sea surface, whereas the contributions of other factors are smoothed by upward continuation (Fan et al, 2021).…”

Regional Seafloor Topography by Extended Kalman Filtering of Marine Gravity Data without Ship-Track Information

“…One of the strategies to decrease the dimensions of the problem, was to decompose the studied oceanic area into overlapping small rectangular cells covering the entire oceanic province, the inversion being made in each of these geographical cells of a few degree dimensions [24,26,33], but with the risk of spatial truncation errors (i.e., loss of signals energy) limited by including ship-track soundings for constraints. Most recent studies have proposed to combine different gravity data using non-linear least square method [34]. But dealing with matrix of large dimensions is required.…”

“…Most recent studies have proposed to combine different gravity data using non-linear least square method [34]. But dealing with matrix of large dimensions is required.…”

Improved the Accuracy of Seafloor Topography from Altimetry-Derived Gravity by the Topography Constraint Factor Weight Optimization Method

“…The accuracy of marine gravity constructed by satellite altimetry has reached 2-3 mGal in most areas [8][9][10][11][12]. Consequently, it promotes the research of seafloor topography inversion [13][14][15][16][17]. Many methods have been proposed for inversion of seabed topography using gravity information, for example, linear response function technique [18], gravity geologic method (GGM) [19], least square configuration method [20] and linear regression analysis [21].…”