| Abstract: | Gravity data inversion can provide valuable information on the structure of the underlying distribution of mass. The solution of the inversion of gravity data is an ill-posed problem, and many methods have been proposed for solving it using various systematic techniques. The method proposed here is a new approach based on the collocation principle, derived from the Wiener filtering and prediction theory. The natural multiplicity of the solution of the inverse gravimetric problem can be overcome only by assuming a substantially simplified model, in this case a two-layer model, i.e. with one separation surface and one density contrast only. The presence of gravity disturbance and/or outliers in the upper layer is also taken into account. The basic idea of the method is to propagate the covariance structure of the depth function of the separation surface to the covariance structure of the gravity field measured on a reference plane. This can be done since the gravity field produced by the layers is a functional (linearized) of the depth. Furthermore, in this approach, it is possible to obtain the variance of the estimation error which indicates the precision of the computed solution. The method has proved to be effective on simulated data, fulfilling the a priori hypotheses. In real cases which display the required statistical homogeneity, good preliminary solutions, useful for a further quantitative interpretation, have also been derived. A case study is discussed. |
