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Abstract

The ability to accurately assess and estimate the uncertainty of the solution to an inverse problem is an important aspect of geophysical inversion. Within this paper, we present a stochastic inversion method for electrical resistivity tomography (ERT) which makes use of Bayesian theory, the reversible-jump Markov chain Monte Carlo algorithm, and model parameterisation with Voronoi cells, to produce an ensemble of valid solutions which are distributed according to the posterior probability density function. By solving the forward problem at each Markov chain iteration and allowing the model cells to vary in number, shape and size throughout the inversion, we ensure that the physics of the forward problem is never linearised, and hence that any parametrisation- and modelling-related bias is naturally reduced to a minimum. In addition, being fully non-linear, this method provides an accurate representation of subsurface resistivity structures as well as a measure of their associated uncertainties. Within this paper, we introduce the theory and method behind our inversion algorithm and present an example of its application to a synthetic dataset. We also benchmark our results by comparing them to those obtained from a more traditional, iterated-linearised inversion scheme.

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/content/papers/10.3997/2214-4609.201602017
2016-09-04
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201602017
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