  Full tensor gravity (FTG) devices provide up to five independent components
of the gravity gradient tensor. However, we do not yet have a quantitative
understanding of which tensor components or combinations of components are more
important to recover a subsurface density model by gravity inversion. This is
mainly because different components may be more appropriate in different
scenarios or purposes. Knowledge of these components in different environments
can aid with selection of optimal selection of component combinations. In this
work, we propose to apply stochastic inversion to assess the uncertainty of
gravity gradient tensor components and their combinations. The method is
therefore a quantitative approach. The applied method here is based on the
geostatistical inversion (Gaussian process regression) concept using cokriging.
The cokriging variances (variance function of the GP) are found to be a useful
indicator for distinguishing the gravity gradient tensor components. This
approach is applied to the New Found dataset to demonstrate its effectiveness
in real-world applications.
