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Article

Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit

1
Computational Geoscience and Reservoir Engineering, RWTH Aachen University, 52062 Aachen, Germany
2
Boliden Mines, 936 32 Boliden, Sweden
*
Author to whom correspondence should be addressed.
Current address: Department of Geoscience and Engineering, Delft University of Technology, 5048 Delft, The Netherlands.
Current address: Dutch State Supervision of Mines, 24037 The Hague, The Netherlands.
§
Current address: Terranigma Solutions GmbH, 52062 Aachen, Germany.
Current address: GRM-Services Ltd., 04360 Tuusula, Finland.
Academic Editors: Maxim Smirnov, Maria Smirnova, Thorkild Maack Rasmussen and Jesus Martinez-Frias
Geosciences 2021, 11(4), 150; https://doi.org/10.3390/geosciences11040150
Received: 22 January 2021 / Revised: 1 March 2021 / Accepted: 8 March 2021 / Published: 26 March 2021
(This article belongs to the Special Issue 3D Modelling/Inversion for Mineral Exploration)
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. View Full-Text
Keywords: 3D modeling; model-based inversion; mineral exploration; airborne magnetics; probabilistic inversion; uncertainty quantification 3D modeling; model-based inversion; mineral exploration; airborne magnetics; probabilistic inversion; uncertainty quantification
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MDPI and ACS Style

Güdük, N.; de la Varga, M.; Kaukolinna, J.; Wellmann, F. Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit. Geosciences 2021, 11, 150. https://doi.org/10.3390/geosciences11040150

AMA Style

Güdük N, de la Varga M, Kaukolinna J, Wellmann F. Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit. Geosciences. 2021; 11(4):150. https://doi.org/10.3390/geosciences11040150

Chicago/Turabian Style

Güdük, Nilgün, Miguel de la Varga, Janne Kaukolinna, and Florian Wellmann. 2021. "Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit" Geosciences 11, no. 4: 150. https://doi.org/10.3390/geosciences11040150

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