Geologic expertise and petrophysical relationships can be brought together to provide prior information while inverting multiple geophysical data sets. The merging of such information can result in more realistic solution in the distribution of the model parameters. We have evaluated the geophysical inverse problem in terms of Gaussian random fields with mean functions controlled by petrophysical relationships and covariance functions controlled by a prior geologic cross section, including the definition of spatial boundaries for the geologic facies. The petrophysical relationship problem is formulated as a regression problem upon each facies. The inversion of the geophysical data is performed in a Bayesian framework. We have developed the usefulness of this strategy using a first synthetic case for which we have performed a joint inversion of gravity and galvanometric resistivity data with the stations located at the ground surface. The joint inversion was used to recover the density and resistivity distributions of the subsurface. In a second step, we considered the possibility that the facies boundaries were deformable and their shapes were inverted as well. We used the level-set approach to perform such deformation preserving a priori topological properties of the facies throughout the inversion. With the help of a priori facies petrophysical relationships and the topological characteristics of each facies, we made a posteriori inference about multiple geophysical tomograms based on their corresponding geophysical data misfits. We have applied this method to a second synthetic case showing that we can recover the heterogeneities inside the facies, the mean values for the petrophysical properties, and, to some extent, the facies boundaries using the 2D joint inversion of gravity and galvanometric resistivity data.

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