Model-based probabilistic inversion using magnetic data

Model-based probabilistic inversion using magnetic data: A case study on the kevitsa deposit

Güdük, N. (TU Delft Applied Geology; Rheinisch-Westfälische Technische Hochschule; Staatstoezicht op de Mijnen)
De La Varga, Miguel (Rheinisch-Westfälische Technische Hochschule; Terranigma Solutions GmbH)
Kaukolinna, Janne (Boliden Mines; GRM-Services Ltd)
Wellmann, Florian (Rheinisch-Westfälische Technische Hochschule)

2021

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.

3D modeling
Airborne magnetics
Mineral exploration
Model-based inversion
Probabilistic inversion
Uncertainty quantification

http://resolver.tudelft.nl/uuid:33ee0bfd-a6c5-4319-ae06-d454d3286ee5

https://doi.org/10.3390/geosciences11040150

2076-3263

Geosciences (Switzerland), 11 (4)

Institutional Repository

journal article

© 2021 N. Güdük, Miguel De La Varga, Janne Kaukolinna, Florian Wellmann