Abstract
The quantitative connections between subsurface geologic structure and measured geophysical data allow 3D geologic models to be tested against measurements and geophysical anomalies to be interpreted in terms of geologic structure. Using a Bayesian framework, geophysical inversions are constrained by prior information in the form of a reference geologic model and probability density functions (pdfs) describing petrophysical properties of the different lithologic units. However, it is challenging to select the probabilistic weights and the structure of the prior model in such a way that the inversion process retains relevant geologic insights from the prior while also exploring the full range of plausible subsurface models. In this study, we investigate how the uncertainty of the prior (expressed using probabilistic constraints on commonality and shape) controls the inferred lithologic and mass density structure obtained by probabilistic inversion of gravimetric data measured at the Krafla geothermal system. We combine a reference prior geologic model with statistics for rock properties (grain density and porosity) in a Bayesian inference framework implemented in the GeoModeller software package. Posterior probability distributions for the inferred lithologic structure, mass density distribution, and uncertainty quantification metrics depend on the assumed geologic constraints and measurement error. As the uncertainty of the reference prior geologic model increases, the posterior lithologic structure deviates from the reference prior model in areas where it may be most likely to be inconsistent with the observed gravity data and may need to be revised. In Krafla, the strength of the gravity field reflects variations in the thickness of hyaloclastite and the depth to high-density basement intrusions. Moreover, the posterior results suggest that a WNW–ESE-oriented gravity low that transects the caldera may be associated with a zone of low hyaloclastite density. This study underscores the importance of reliable prior constraints on lithologic structure and rock properties during Bayesian geophysical inversion.
Highlights
Three-dimensional (3D) geologic models illustrate the spatial distribution of subsurface lithologic units and major structural features such as faults, and visually convey geoscientific understanding during the process of resource assessment and conceptual model development
We investigate how the uncertainty of the prior controls the inferred lithologic and mass density structure obtained by probabilistic inversion of gravimetric data measured at the Krafla geothermal system
We present results for posterior probability distributions and uncertainty quantification metrics along the three cross sections shown in Fig. 3 (Section 1, a N–S section going through Vitismor, Leirbotnar, and ending north of Hvítholar; Section 2, a SW– NE section going through Vestursvaeð i, Leirbotnar, Hveragil and Vesturhlíðar; and Section 3, a NW–SE section going through Leirhnjúkur, Vitismor, Leirbotnar, Hveragil and Suðurhilíðar)
Summary
Three-dimensional (3D) geologic models illustrate the spatial distribution of subsurface lithologic units and major structural features such as faults, and visually convey geoscientific understanding during the process of resource assessment and conceptual model development. Geophysical data sets (e.g., gravity potential field) may be calculated directly from 3D geologic models by assigning values for petrophysical properties (e.g., density) to the subsurface rock units and calculating the forward gravity model. Geophysical inversion results describing the distribution of rock types and physical property values in the subsurface are uncertain, as an infinite number of possible models of the subsurface may account for the measured data. While the integration of geologic constraints and prior information such as rock petrophysical properties are necessary to ensure the inversion process retains geologic meaning, overly strong prior constraints may lead to an underestimation of uncertainty in the final results. Quantifying how the degree of confidence in the prior understanding of geothermal system structure affects results from probabilistic inversion methods is necessary for these approaches to contribute to effective risk management in geothermal resource assessment (United Nations Economic Commission for Europe Expert Group on Resource Classification 2017)
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