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Stochastic structure-constrained image-guided inversion of geophysical data

Abstract : Inverse modeling of geophysical data involves the recovery of a subsurface structural model and the distribution of petrophysical properties. Independent information regarding the subsurface structure is usually available, with some uncertainty, from the expertise of a geologist and possibly accounting for sedimentary and tectonic processes. We have used the available structural information to construct a model covariance matrix and to perform a structure-constrained inversion of the geophysical data to obtain a geophysical tomogram. We have considered that the geologic models were built from random variables and were described with a priori probability density function in the Bayesian framework. We have explored for the a posteriori probability density of the geologic models (i.e., the structure of the guiding image) with the Markov-chain Monte Carlo method, and we inverted at the same time, in a deterministic framework, the geophysical data. The sampling of the geologic models was performed in a stochastic framework, and each geologic model was used to invert the geophysical model using image-guided inversion. The adaptive metropolis algorithm was used to find the proposal distributions of reproducing the geophysical data and the geophysical information. In other words, we have tried to find a compromise between the a priori geologic information and the geophysical data to get, as end products, an updated geologic model and a geophysical tomogram. To demonstrate our approach, we used here electrical resistivity tomography as a technique to identify a correct geologic model and its a posteriori probability density. The approach was tested using one synthetic example (with three horizontal layers displaced by a normal fault) and one field case corresponding to a sinkhole in a three-layer structure. In both cases, we were able to select the most plausible geologic models that agreed with a priori information and the geophysical data.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-01742268
Contributor : Pierre Weill <>
Submitted on : Saturday, March 24, 2018 - 12:59:36 PM
Last modification on : Tuesday, October 6, 2020 - 8:44:02 AM

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Jieyi Zhou, André Revil, Abderrahim Jardani. Stochastic structure-constrained image-guided inversion of geophysical data. Geophysics, Society of Exploration Geophysicists, 2016, 81 (2), pp.E89--E101. ⟨10.1190/geo2014-0569.1⟩. ⟨hal-01742268⟩

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