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Summary statistics from training images as prior information in probabilistic inversion

Lochbühler, Tobias ; Vrugt, Jasper A. ; Sadegh, Mojtaba ; Linde, Niklas

In: Geophysical Journal International, 2015, vol. 201, no. 1, p. 157-171

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    Titel
    • Summary statistics from training images as prior information in probabilistic inversion
    Autor
    • Lochbühler, Tobias. Applied and Environmental Geophysics Group, Institute of Earth Sciences, University of Lausanne, 1015 Lausanne, Switzerland. E-mail: Niklas.Linde@unil.ch
    • Vrugt, Jasper A.. Department of Civil and Environmental Engineering, University of California Irvine, 4130 Irvine, CA, USA
    • Sadegh, Mojtaba. Department of Civil and Environmental Engineering, University of California Irvine, 4130 Irvine, CA, USA
    • Linde, Niklas. Applied and Environmental Geophysics Group, Institute of Earth Sciences, University of Lausanne, 1015 Lausanne, Switzerland. E-mail: Niklas.Linde@unil.ch
    Dokumententyp
    • Postprint
    Sprache
    • Englisch
    Veröffentlicht in
    • Geophysical Journal International, 2015, vol. 201, no. 1, p. 157-171. Oxford University Press
    Andere elektronische Ausgabe
    OAI-PMH-ID
    • oai:doc.rero.ch:293674
    Summary
    A strategy is presented to incorporate prior information from conceptual geological models in probabilistic inversion of geophysical data. The conceptual geological models are represented by multiple-point statistics training images (TIs) featuring the expected lithological units and structural patterns. Information from an ensemble of TI realizations is used in two different ways. First, dominant modes are identified by analysis of the frequency content in the realizations, which drastically reduces the model parameter space in the frequency-amplitude domain. Second, the distributions of global, summary metrics (e.g. model roughness) are used to formulate a prior probability density function. The inverse problem is formulated in a Bayesian framework and the posterior pdf is sampled using Markov chain Monte Carlo simulation. The usefulness and applicability of this method is demonstrated on two case studies in which synthetic crosshole ground-penetrating radar traveltime data are inverted to recover 2-D porosity fields. The use of prior information from TIs significantly enhances the reliability of the posterior models by removing inversion artefacts and improving individual parameter estimates. The proposed methodology reduces the ambiguity inherent in the inversion of high-dimensional parameter spaces, accommodates a wide range of summary statistics and geophysical forward problems