  A strategy is presented to incorporate prior information from conceptual
geological models in probabilistic inversion of geophysical data. The
conceptual geological models are represented by multiple-point statistics
training images (TIs) featuring the expected lithological units and structural
patterns. Information from an ensemble of TI realizations is used in two
different ways. First, dominant modes are identified by analysis of the
frequency content in the realizations, which drastically reduces the model
parameter space in the frequency-amplitude domain. Second, the distributions of
global, summary metrics (e.g. model roughness) are used to formulate a prior
probability density function. The inverse problem is formulated in a Bayesian
framework and the posterior pdf is sampled using Markov chain Monte Carlo
simulation. The usefulness and applicability of this method is demonstrated on
two case studies in which synthetic crosshole ground-penetrating radar
traveltime data are inverted to recover 2-D porosity fields. The use of prior
information from TIs significantly enhances the reliability of the posterior
models by removing inversion artefacts and improving individual parameter
estimates. The proposed methodology reduces the ambiguity inherent in the
inversion of high-dimensional parameter spaces, accommodates a wide range of
summary statistics and geophysical forward problems.

Geophysics;Methodology