Probabilistic inversion with graph cuts: Application to the Boise Hydrogeophysical Research Site

Details

Serval ID
serval:BIB_A2D1E089A931
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Probabilistic inversion with graph cuts: Application to the Boise Hydrogeophysical Research Site
Journal
Water Resources Research
Author(s)
Pirot G., Linde N., Mariethoz G., Bradford J.H.
ISSN
1944-7973
Publication state
Published
Issued date
2017
Peer-reviewed
Oui
Volume
53
Pages
1231–1250
Language
english
Abstract
Inversion methods that build on multiple-point statistics tools offer the possibility to obtain model realizations that are not only in agreement with field data, but also with conceptual geological models that are represented by training images. A recent inversion approach based on patch-based geostatistical resimulation using graph cuts outperforms state-of-the-art multiple-point statistics methods when applied to synthetic inversion examples featuring continuous and discontinuous property fields. Applications of multiple-point statistics tools to field data are challenging due to inevitable discrepancies between actual subsurface structure and the assumptions made in deriving the training image. We introduce several amendments to the original graph cut inversion algorithm and present a first-ever field application by addressing porosity estimation at the Boise Hydrogeophysical Research Site, Boise, Idaho. We consider both a classical multi-Gaussian and an outcrop-based prior model (training image) that are in agreement with available porosity data. When conditioning to available crosshole ground-penetrating radar data using Markov chain Monte Carlo, we find that the posterior realizations honor overall both the characteristics of the prior models and the geophysical data. The porosity field is inverted jointly with the measurement error and the petrophysical parameters that link dielectric permittivity to porosity. Even though the multi-Gaussian prior model leads to posterior realizations with higher likelihoods, the outcrop-based prior model shows better convergence. In addition, it offers geologically more realistic posterior realizations and it better preserves the full porosity range of the prior.
Keywords
Bayesian Inversion, Graph Cuts, Multiple-Point Statistics, MPS, Geological Realism, Markov chain Monte Carlo, MCMC, Boise Hydrogeophysical Research Site, BHRS, Ground Penetrating Radar, GPR
Create date
27/07/2017 16:04
Last modification date
20/08/2019 15:08
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