We introduce and test a workflow that integrates petrophysical constraints and geological data in geophysical inversion in order to decrease the effect of non-uniqueness and to improve imaging. This workflow uses petrophysical measurements to constrain the values retrieved by geophysical inversion. Geological modelling is used to define petrophysical constraints spatially and to provide starting models. We integrate the different sources of information in a Bayesian framework that quantitatively unifies geological modelling, petrophysical measurements and geophysical data. It accounts for the levels of prior knowledge related the various sources of information. Inversion modifies the model accordingly to honor the different datasets. This methodology was tested using synthetic datasets in order to validate the methodology and to assess its robustness, for gravity and magnetic data. The results show that the use of petrophysical constraints during inversion increases contrasts in inverted models. Prior structural information from geological modelling allows for better retrieval of the geometry of geological structures. Overall, the integration of the different constraints reduces model misfit and provides geologically consistent geometries.