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A probabilistic geologic model of the Krafla geothermal system constrained by gravimetric data

A probabilistic geologic model of the Krafla geothermal system constrained by gravimetric data

Title: A probabilistic geologic model of the Krafla geothermal system constrained by gravimetric data
Author: Scott, Samuel   orcid.org/0000-0001-7608-7358
Covell, Cari
Júlíusson, Egill
Valfells, Agust   orcid.org/0000-0002-6561-4957
Newson, Juliet
Hrafnkelsson, Birgir   orcid.org/0000-0003-1864-9652
Pálsson, Halldór
Gudjónsdóttir, María   orcid.org/0000-0001-7577-1190
Date: 2019-09-24
Language: English
Scope: 29
University/Institute: Háskóli Íslands
University of Iceland
Háskólinn í Reykjavík
Reykjavik University
School: School of Engineering and Natural Sciences (UI)
Verkfræði- og náttúruvísindasvið (HÍ)
School of Technology (RU)
Tæknisvið (HR)
Department: Verkfræðideild (HR)
Department of Engineering (RU)
Series: Geothermal Energy;7(1)
ISSN: 2195-9706
DOI: 10.1186/s40517-019-0143-6
Subject: Bayesian inference; Geologic modeling; Gravity; Iceland; Líkindafræði; Jarðhitasvæði; Krafla; Þyngdarafl; Líkanagerð
URI: https://hdl.handle.net/20.500.11815/1534

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Scott, S.W., Covell, C., Júlíusson, E. et al. A probabilistic geologic model of the Krafla geothermal system constrained by gravimetric data. Geothermal Energy 7, 29 (2019). https://doi.org/10.1186/s40517-019-0143-6


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.


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Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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