3D unconstrained and geologically constrained stochastic inversion of airborne vertical gravity gradient data

Abstract

We present an inversion tool for airborne gravity gradient data that yields a 3D density model using stochastic methods i.e. cokriging and conditional simulation. This method uses geostatistical properties of the measured gravity gradient to estimate a 3D density model whose gravity response fits the measured gravity gradient anomaly. Linearity between gravity gradient data and density allows estimation of the model (density) covariance using observed data, i.e. we adjust iteratively the density covariance matrix by fitting experimental and theoretical gravity gradient covariance matrices. Inversion can be constrained by including densities known at some locations. In addition we can explore various reasonable solutions that honour both the estimated density covariance model and the gravity gradient data using geostatistical simulation.The proposed method is first tested with two synthetic datasets generated from a sharp-boundary model and a smooth stochastic model respectively. The results show the method to be capable of retrieving models compatible with the true models; it also allows the integration of complex a priori information. The technique is then applied to gravity gradient survey data collected for the Geological Survey of Canada in the area of McFaulds Lake (Ontario, Canada) using the Falcon airborne gravity system. Unconstrained inversion returns a density model that is geologically plausible and the computed response exactly fits the observed gravity gradient anomaly.