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

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.

Automated geological model updates during TBM operation – An approach based on probabilistic machine learning concepts

“…The geomodelling code GemPy (www.gempy.org) [9] fulfils this criterion and can readily be integrated into the workflow shown here. GemPy has already been used for probabilistic inference methods using complex 3D geological models and gravity [20] and magnetic [8] surface measurements. An extension to TBM measurement data would be possible through the framework developed in the work presented here.…”

“…These uncertainties can be considered in 3D geological workflows [5]. With additional information, uncertainties can potentially be reduced and predictions optimized [7,8].…”

An overview of multimethod imaging approaches in environmental geophysics

“…Alternatively, the position of a sharp interface may be estimated during the joint inversion, as demonstrated in the context of bedrock detection by means of probabilistic joint inversion of electrical resistivity and seismic refraction data (de Pasquale et al, 2019). We expect that the recent availability of open and versatile geological modeling tools (e.g., de la Varga et al, 2019) will pave the way for an improved amalgamation of geophysical and geological data in joint structure-based inversion frameworks (e.g., Güdük et al, 2021;Förderer et al, 2021).…”

Informed Local Smoothing in 3D Implicit Geological Modeling

“…Unconformable relations are modeled by combinations of multiple scalar fields, while faults are represented by drift functions in the cokriging system [4,6]. The method is implemented in a range of software packages and has been successfully applied in various case studies to investigate crustal architectures (see, e.g., in [8][9][10]), geothermal and hydrogeological settings (e.g., [11,12]) and mineral systems (see, e.g., in [13][14][15]).…”