Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization

TitleBayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization
Publication TypeJournal Article
Year of Publication2014
AuthorsRay A., Key K, Bodin T., Myer D., Constable S
JournalGeophysical Journal International
Volume199
Pagination1847-1860
Date Published2014/12
Type of ArticleArticle
ISBN Number0956-540X
Accession NumberWOS:000345509800041
Keywordsalgorithm; australia; chain monte-carlo; electromagnetic data; error models; geoacoustic inversion; Inverse theory; magnetotelluric data; Marine electromagnetics; markov-chains; Non-linear electromagnetics; Probability distributions; seismic ava; shallow-water problem; stochastic inversion
Abstract

We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian posterior model probability density function of 2-D seafloor resistivity as constrained by marine controlled source electromagnetic data. This density function of earth models conveys information on which parts of the model space are illuminated by the data. Whereas conventional gradient-based inversion approaches require subjective regularization choices to stabilize this highly non-linear and non-unique inverse problem and provide only a single solution with no model uncertainty information, the method we use entirely avoids model regularization. The result of our approach is an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of resolution of model parameters. We represent models in 2-D using a Voronoi cell parametrization. To make the 2-D problem practical, we use a source-receiver common midpoint approximation with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where the number of resistivity cells within a 2-D depth section is variable, as are their positions and geometries. Two synthetic studies demonstrate the algorithm's use in the appraisal of a thin, segmented, resistive reservoir which makes for a challenging exploration target. As a demonstration example, we apply our method to survey data collected over the Scarborough gas field on the Northwest Australian shelf.

DOI10.1093/gji/ggu370
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Student Publication: 
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