Finding seamounts with altimetry-derived gravity data

Seamounts are ubiquitous manifestations of underwater volcanism that rise above the surrounding ocean floor by more than a few hundred or thousand meters. Any temporal and spatial variations of the underwater volcanic and tectonic processes that formed seamounts can primarily be understood through their geometric characterization and spatial distribution. For this study, we utilize the vertical gravity gradient (VGG) version 23.1 derived from satellite altimetry, which includes new data from the CryoSat-2, Envisat, and Jason1 missions. A repeated statistical comparison for an area with no significant geologic features shows that the standard deviation of VGG 23.1 is decreased about 48% from the previous release, indicating the signal-to-noise ratio has been improved significantly from the previous version. In order to examine whether the new data give us better opportunities to find seamounts, we choose near-ridge environments constrained by good bathymetry coverage. For a given area, the nonlinear inversion method to search for seamounts is applied. We approximate VGG anomalies over seamounts as sums of individual, partially overlapping, elliptical polynomial functions, which allows us to form a non-linear inverse problem by fitting the polynomial model to the observations. Model parameters for a potential seamount include geographical location, peak VGG amplitude, major and minor axes of the elliptical base, and the azimuth of the major axis. The non-linear inversion is very sensitive to the initial values for the location and amplitude; hence, they are constrained by the center and amplitude of the uppermost contours obtained with a 1-Eotvos contour interval. With these initial conditions from contouring, we execute a stepwise and fully automated inversion and obtain optimal model estimates for potential seamounts; these are statistically evaluated for significance using the Akaike Information Criterion and F tests. Here we present a preliminary result of seamount detection using the new global data and discuss possibilities for constructing a new synthesized global dataset of seamounts.