Premium
Bayesian inversion of geoelectrical resistivity data
Author(s) -
Andersen Kim E.,
Brooks Stephen P.,
Hansen Martin B.
Publication year - 2003
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00406
Subject(s) - markov chain monte carlo , inversion (geology) , computer science , bayesian probability , random field , algorithm , data mining , gaussian , metropolis–hastings algorithm , monte carlo method , data set , geology , artificial intelligence , mathematics , statistics , paleontology , physics , structural basin , quantum mechanics
Summary. Enormous quantities of geoelectrical data are produced daily and often used for large scale reservoir modelling. To interpret these data requires reliable and efficient inversion methods which adequately incorporate prior information and use realistically complex modelling structures. We use models based on random coloured polygonal graphs as a powerful and flexible modelling framework for the layered composition of the Earth and we contrast our approach with earlier methods based on smooth Gaussian fields. We demonstrate how the reconstruction algorithm may be efficiently implemented through the use of multigrid Metropolis–coupled Markov chain Monte Carlo methods and illustrate the method on a set of field data.