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dc.contributor.advisorCurtis, Andrew
dc.contributor.advisorChapman, Mark
dc.contributor.authorWalker, Matthew James
dc.date.accessioned2015-08-27T14:17:56Z
dc.date.available2015-08-27T14:17:56Z
dc.date.issued2015-06-30
dc.identifier.urihttp://hdl.handle.net/1842/10504
dc.description.abstractThe purpose of Bayesian seismic inversion is to combine information derived from seismic data and prior geological knowledge to determine a posterior probability distribution over parameters describing the elastic and geological properties of the subsurface. Typically the subsurface is modelled by a cellular grid model containing thousands or millions of cells within which these parameters are to be determined. Thus such inversions are computationally expensive due to the size of the parameter space (being proportional to the number of grid cells) over which the posterior is to be determined. Therefore, in practice approximations to Bayesian seismic inversion must be considered. A particular, existing approximate workflow is described in this thesis: the so-called two-stage inversion method explicitly splits the inversion problem into elastic and geological inversion stages. These two stages sequentially estimate the elastic parameters given the seismic data, and then the geological parameters given the elastic parameter estimates, respectively. In this thesis a number of methodologies are developed which enhance the accuracy of this approximate workflow. To reduce computational cost, existing elastic inversion methods often incorporate only simplified prior information about the elastic parameters. Thus a method is introduced which transforms such results, obtained using prior information specified using only two-point geostatistics, into new estimates containing sophisticated multi-point geostatistical prior information. The method uses a so-called deep neural network, trained using only synthetic instances (or `examples') of these two estimates, to apply this transformation. The method is shown to improve the resolution and accuracy (by comparison to well measurements) of elastic parameter estimates determined for a real hydrocarbon reservoir. It has been shown previously that so-called mixture density network (MDN) inversion can be used to solve geological inversion analytically (and thus very rapidly and efficiently) but only under certain assumptions about the geological prior distribution. A so-called prior replacement operation is developed here, which can be used to relax these requirements. It permits the efficient MDN method to be incorporated into general stochastic geological inversion methods which are free from the restrictive assumptions. Such methods rely on the use of Markov-chain Monte-Carlo (MCMC) sampling, which estimate the posterior (over the geological parameters) by producing a correlated chain of samples from it. It is shown that this approach can yield biased estimates of the posterior. Thus an alternative method which obtains a set of non-correlated samples from the posterior is developed, avoiding the possibility of bias in the estimate. The new method was tested on a synthetic geological inversion problem; its results compared favourably to those of Gibbs sampling (a MCMC method) on the same problem, which exhibited very significant bias. The geological prior information used in seismic inversion can be derived from real images which bear similarity to the geology anticipated within the target region of the subsurface. Such so-called training images are not always available from which this information (in the form of geostatistics) may be extracted. In this case appropriate training images may be generated by geological experts. However, this process can be costly and difficult. Thus an elicitation method (based on a genetic algorithm) is developed here which obtains the appropriate geostatistics reliably and directly from a geological expert, without the need for training images. 12 experts were asked to use the algorithm (individually) to determine the appropriate geostatistics for a physical (target) geological image. The majority of the experts were able to obtain a set of geostatistics which were consistent with the true (measured) statistics of the target image.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionMatthew Walker and Andrew Curtis 2014 Inverse Problems 30 065002 doi:10.1088/0266-5611/30/6/065002en
dc.relation.hasversionWalker, M., and A. Curtis (2014), Spatial Bayesian inversion with localized likelihoods: An exact sampling alternative to MCMC, J. Geophys. Res. Solid Earth, 119, 5741–5761, doi:10.1002/2014JB011010.en
dc.relation.hasversionMatthew Walker and Andrew Curtis, Expert elicitation of geological spatial statistics using genetic algorithms, Geophys. J. Int. (2014)en
dc.subjectBayesen
dc.subjectseismic inversionen
dc.subjectgeostatisticsen
dc.subjectMCMCen
dc.subjectMarkov-chain Monte-Carloen
dc.titleMethods for Bayesian inversion of seismic dataen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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