Towards 3D probabilistic inversion with graphcuts

N. Linde, M. Cardiff, G. Mariethoz, J. Bradford, G. Pirot

Research output: Chapter in Book/Conference paperConference paperpeer-review

2 Citations (Scopus)


For appropriate uncertainty quantification in hydrogeological applications (e.g., contaminant plume forecasting), it is essential to infer subsurface models that feature geologically realistic geometries and property contrasts. Recently, an efficient multiple-point statistics probabilistic inversion approach, with model proposals based on graph cuts, has been shown to provide posterior model realizations that honor pre-defined geometrical shapes and property contrasts. It has been tested for both synthetic and field examples involving crosshole groundpenetrating radar. Here, we present the approach and proceed with initial findings on how to extend this method to 3D and hydraulic tomography data. Improvements and modifications in the Markov chain Monte Carlo algorithm are proposed that allow for appropriate acceptance and convergence rates. We also discuss possible ways to circumvent long computing times, for example, by including physical approximations and machine learning techniques, or to focus on global optimization rather than Bayesian posterior sampling.

Original languageEnglish
Title of host publication23rd European Meeting of Environmental and Engineering Geophysics
PublisherEuropean Association of Geoscientists and Engineers, EAGE
ISBN (Electronic)9789462822238
Publication statusPublished - 2017
Externally publishedYes
Event23rd European Meeting of Environmental and Engineering Geophysics - Malmo, Sweden
Duration: 3 Sept 20177 Sept 2017


Conference23rd European Meeting of Environmental and Engineering Geophysics
Internet address


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