Joint Sequential Simulation of MultiGaussian Fields
The sequential simulation algorithm can be used for the generation of conditional realizations from either a multiGaussian random function or any non-Gaussian random function as long as its conditional distributions can be derived. The multivariate probability density function (pdf) that fully describes a random function can be written as the product of a set of univariate conditional pdfs. Drawing realizations from the multivariate pdf amounts to drawing sequentially from that series of univariate conditional pdfs. Similarly, the joint multivariate pdf of several random functions can be written as the product of a series of univariate conditional pdfs. The key step consists of the derivation of the conditional pdfs. In the case of a multiGaussian fields, these univariate conditional pdfs are known to be Gaussian with mean and variance given by the solution of a set of normal equations also known as simple cokriging equations. Sequential simulation is preferred to other techniques, such as turning bands, because of its ease of use and extreme flexibility.
KeywordsConditional Distribution Search Neighborhood Exponential Type Sequential Simulation Conditioning Data
Unable to display preview. Download preview PDF.
- Alabert, F. G. (1987). Stochastic imaging of spatial distributions using hard and soft data. Master’s thesis, Stanford University, CA, Branner Earth Sciences Library.Google Scholar
- Anderson, T. W. (1984). Multivariate statistical analysis. Wiley, New York.Google Scholar
- Gómez-Hernández, J. J. (1991). A Stochastic Approach to the Simulation of Block Conductivity Values Conditioned Upon Data Measured at a Smaller Scale. PhD thesis, Stanford University.Google Scholar
- Gómez-Hernández, J. J. (1992). Regularization of hydraulic conductivities: A numerical approach. In Proc. of the 4th Int. Geostat. Congress.Google Scholar