3d Inversion Of Airborne Vertical Gradient Gravity Data

We present an inversion method based on a geostatistical approach, i.e. cokriging and conditional simulation for three dimensional inversion of airborne gradient gravity data including geological constraints. Cokriging is a method of estimation that minimizes the error variance by applying cross-correlation between several variables. In this study the estimates are derived using gradient gravity data as secondary variable and the density as the primary variable. In the proposed method, the linearity between gradient gravity and density allows us to obtain a covariance matrix of densities using observed data, i.e, we adjust the density covariance matrix by fitting experimental and theoretical gradient gravity covariance matrices. To obtain various reasonable solutions in order to see the variability that can be expected from the density covariance model adopted, a geostatistical simulation algorithm is applied. The proposed method was first tested on synthetic data. The result shows the ability of the method to integrate complex a priori information. The technique was then applied to actual gravity gradient data collected by the Geological Survey of Canada in the area of Strange-Lake (Quebec) using the Falcon gravity system. Results of inversion (cokriging and co-simulation) are in good agreement with the geology of the studied regions.