Abstract
Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of data consistently and consider the uncertainties quantitatively. Probabilistic inference provides a suitable tool for this purpose. Using a Bayesian framework, geological modeling can be considered as an integral part of the inversion and thereby naturally constrain geophysical inversion procedures. This integration prevents geologically unrealistic results and provides the opportunity to include geological and geophysical information in the inversion. This information can be from different sources and is added to the framework through likelihood functions. We applied this methodology to the structurally complex Kevitsa deposit in Finland. We started with an interpretation-based 3D geological model and defined the uncertainties in our geological model through probability density functions. Airborne magnetic data and geological interpretations of borehole data were used to define geophysical and geological likelihoods, respectively. The geophysical data were linked to the uncertain structural parameters through the rock properties. The result of the inverse problem was an ensemble of realized models. These structural models and their uncertainties are visualized using information entropy, which allows for quantitative analysis. Our results show that with our methodology, we can use well-defined likelihood functions to add meaningful information to our initial model without requiring a computationally-heavy full grid inversion, discrepancies between model and data are spotted more easily, and the complementary strength of different types of data can be integrated into one framework.
Highlights
IntroductionData-driven approaches are used where the subsurface is divided into grid cells to which petrophysical properties are assigned
To avoid geologically unrealistic outcomes and to omit the influence of the gridding, geophysical inversion can be naturally constrained by surfacebased modeling, where a rock property is assigned to a surface
We present a methodology to reduce the dimensionality of geophysical inversions by introducing prior structural geological modeling as an informative constrain in a Bayesian framework
Summary
Data-driven approaches are used where the subsurface is divided into grid cells to which petrophysical properties are assigned. To avoid geologically unrealistic outcomes and to omit the influence of the gridding, geophysical inversion can be naturally constrained by surfacebased modeling, where a rock property is assigned to a surface. By considering the structural geological model as part of the inference, rock property and subsurface geometry can be integrated. This integration is essential to understanding mineral systems, where there is a complex interaction of different physical processes. It is known that geological structure plays an important role in this (e.g., [4,5,6]) and ideally geological, geophysical and geochemical data would have to be considered together. Though not straightforward in its implementation, becomes increasingly relevant as data acquisition is getting cheaper and faster, and often large amounts of different types of data are available
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