Data Integration using the Probability Perturbation Method

  • Jef Caers
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 14)

A new method, termed probability perturbation, is developed for solving nonlinear inverse problem under a prior model constraint. The method proposed takes a different route from the traditional Bayesian inverse models that rely on prior and likelihood distribution for stating, then sampling, from a posterior distribution. Instead, the probability perturbation method relies on so-called pre-posterior distributions, which state the distribution of the unknown parameter set given each individual data type (linear or non-linear). Sampling consists of perturbing the probability models used to generate the model realization, by which a chain of realizations is created that converge to match any type of data. The probability perturbations are such that the underlying spatial structure (prior model) of the stochastic algorithm is maintained through all perturbations. A simple example illustrates the approach.


Forward Model Conditional Independence History Match Markov Chain Monte Carlo Method Sequential Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2005

Authors and Affiliations

  • Jef Caers
    • 1
  1. 1.Department of Petroleum EngineeringStanford UniversityStanfordUSA

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