The spatial relationship between different rock types and relevant structural features is an important aspect in the characterization of ore-forming systems. Our knowledge about this geological architecture is often captured in 3D structural geological models. Multiple methods exist to generate these models, but one important problem remains: structural models often contain significant uncertainties. In recent years, several approaches have been developed to consider uncertainties in geological prior parameters that are used to create these models through the use of stochastic simulation methods. However, a disadvantage of these methods is that there is no guarantee that each simulated model is geologically reasonable – and that it forms a valid representation in the light of additional data (e.g. geophysical measurements). We address these shortcomings here with an approach for the integration of structural geological and geophysical data into a framework that explicitly considers model uncertainties. We combine existing implicit structural modelling methods with novel developments in probabilistic programming in a Bayesian framework. In an application of these concepts to a gold-bearing greenstone belt in Western Australia, we show that we are able to significantly reduce uncertainties in the final model by additional data integration. Although the final question always remains whether a predicted model suite is a suitable representation of accuracy or not, we conclude that our application of a Bayesian framework provides a novel quantitative approach to addressing uncertainty and optimization of model parameters.

Supplementary material: Trace plots for selected parameters and plots of calculated Geweke statistics are available at https://doi.org/10.6084/m9.figshare.c.3899719