Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data
Détails
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Etat: Public
Version: Final published version
Licence: Non spécifiée
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.
Etat: Public
Version: Final published version
Licence: Non spécifiée
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.
ID Serval
serval:BIB_B3E565F3E65A
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data
Périodique
Geophysical Journal International
ISSN-L
0956-540X
Statut éditorial
Publié
Date de publication
2014
Peer-reviewed
Oui
Volume
196
Pages
1508-1524
Langue
anglais
Résumé
Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.
Site de l'éditeur
Open Access
Oui
Création de la notice
25/11/2013 18:41
Dernière modification de la notice
14/02/2022 7:56