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

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor Háskólinn í Reykjavík
dc.contributor Reykjavik University
dc.contributor.advisor
dc.contributor.author Scott, Samuel
dc.contributor.author Covell, Cari
dc.contributor.author Júlíusson, Egill
dc.contributor.author Valfells, Águst
dc.contributor.author Newson, Juliet
dc.contributor.author Hrafnkelsson, Birgir
dc.contributor.author Pálsson, Halldór
dc.contributor.author Gudjónsdóttir, María
dc.date.accessioned 2020-02-12T15:11:29Z
dc.date.available 2020-02-12T15:11:29Z
dc.date.issued 2019-09-24
dc.identifier.citation 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
dc.identifier.issn 2195-9706
dc.identifier.uri https://hdl.handle.net/20.500.11815/1534
dc.description Publisher's version (útgefin grein).
dc.description.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.
dc.description.sponsorship Icelandic Centre for Research. This study was funded by Technical Development Fund of the Research Center of Iceland (RANNÍS—Grant Number 175193-0612 Data Fusion for Geothermal Reservoir Characterization).
dc.format.extent 29
dc.language.iso en
dc.publisher Springer Science and Business Media LLC
dc.relation.ispartofseries Geothermal Energy;7(1)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Bayesian inference
dc.subject Geologic modeling
dc.subject Gravity
dc.subject Iceland
dc.subject Líkindafræði
dc.subject Jarðhitasvæði
dc.subject Krafla
dc.subject Þyngdarafl
dc.subject Líkanagerð
dc.title A probabilistic geologic model of the Krafla geothermal system constrained by gravimetric data
dc.type info:eu-repo/semantics/article
dcterms.license 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.
dc.description.version Peer Reviewed
dc.identifier.journal Geothermal Energy
dc.identifier.doi 10.1186/s40517-019-0143-6
dc.contributor.department Verkfræðideild (HR)
dc.contributor.department Department of Engineering (RU)
dc.contributor.school School of Engineering and Natural Sciences (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Techology (RU)
dc.contributor.school Tæknisvið (HR)


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