Rapid Imaging of Upper Mantle Density Variation with Satellite Gravity Gradients

Probabilstic linear inversion of satellite gravity gradient tensor data based on statistical prior information is applied to image density heterogeneity in the upper mantle assuming a Gaussian model. The weighting function required for the inversion is derived based on the integrated sensitivity kernel for each individual tensor component in spherical coordinates. The uncertainty of the residual gravity gradient signal is characterized by a covariance matrix obtained using geostatistical analysis of controlled-source seismic data. The forward modeling of the gravity gradients in the 3D reference crustal model is performed using a global spherical harmonics analysis. We estimate the model covariance function in the radial and angular directions using a variogram method and compute volumetric gravity gradient kernels for a spherical shell covering the target region down to the mantle transition zone (410 km depth). The solution of the linear inverse problem in the form of the mean density model and the posterior covariance matrix follows a least squares approach. The method is applied to the North Atlantic and the Northeast Pannonian Basin. In the areas of mantle upwelling, the radial gravity gradient correlates with dynamic topography. The mantle thermal density variation is estimated based on seismic velocities, and the differences with the gravity-derived density anomalies are interpreted in terms of lithospheric compositional changes.