Taking advantage of all available information including geological and geophysical datasets is important to understand the subsurface. Integration of geological and geophysical datasets is commonly limited to the interpretation of areaspecific case studies and has seldom been addressed by automated techniques. Considering, the geometry of rock units as the unknown for both problems, one advantage of the automation of integration approaches is the parametrization of the subsurface which can be identical for both geological modelling and geophysical inversion. In this study, we introduce an approach and application that allows us to automate geophysical inversion workflow with an implicit geological modelling engine. We consider the contacts between units as the quantity linking geology and geophysics and generate a model from the geometry of the rock unit boundaries. We use a generalized level-set inversion algorithm integrating the implicit geological modelling engine called LoopStructural, which is used to define a regularisation correction term for geophysical inversion. The geological plausibility of the inverted model is continually evaluated and ensured during inversion based on correction factors from geology. Constraints from other geophysical datasets or existing knowledge are also encapsulated in weighting matrices through constraining terms. The introduced methodology has been tested in the Boulia region (Southern Mount Isa, Queensland), using several 2D seismic transects, bore-hole density measurements, and high-resolution gravity datasets. This study focuses on recovering the geometry of major structures of the basement units on a regional scale. The utilisation of seismic profiles and gravity datasets with geological modelling allows us to generate a 3D model of the area containing distinct rock units and boundary geometries that realistic gravity and seismic responses. The legacy datasets and existing results from the previous modelling have been utilised and adjusted ensuring that the final model follows all known information about the region.

Open-Access Online Publication: May 29, 2023