Mathematical Geology

, Volume 32, Issue 8, pp 919–942

Geostatistical Simulation of Regionalized Pore-Size Distributions Using Min/Max Autocorrelation Factors

  • A. J. Desbarats
  • R. Dimitrakopoulos

DOI: 10.1023/A:1007570402430

Cite this article as:
Desbarats, A.J. & Dimitrakopoulos, R. Mathematical Geology (2000) 32: 919. doi:10.1023/A:1007570402430


In many fields of the Earth Sciences, one is interested in the distribution of particle or void sizes within samples. Like many other geological attributes, size distributions exhibit spatial variability, and it is convenient to view them within a geostatistical framework, as regionalized functions or curves. Since they rarely conform to simple parametric models, size distributions are best characterized using their raw spectrum as determined experimentally in the form of a series of abundance measures corresponding to a series of discrete size classes. However, the number of classes may be large and the class abundances may be highly cross-correlated. In order to model the spatial variations of discretized size distributions using current geostatistical simulation methods, it is necessary to reduce the number of variables considered and to render them uncorrelated among one another. This is achieved using a principal components-based approach known as Min/Max Autocorrelation Factors (MAF). For a two-structure linear model of coregionalization, the approach has the attractive feature of producing orthogonal factors ranked in order of increasing spatial correlation. Factors consisting largely of noise and exhibiting pure nugget–effect correlation structures are isolated in the lower rankings, and these need not be simulated. The factors to be simulated are those capturing most of the spatial correlation in the data, and they are isolated in the highest rankings. Following a review of MAF theory, the approach is applied to the modeling of pore-size distributions in partially welded tuff. Results of the case study confirm the usefulness of the MAF approach for the simulation of large numbers of coregionalized variables.

principal component analysis coregionalization 

Copyright information

© International Association for Mathematical Geology 2000

Authors and Affiliations

  • A. J. Desbarats
    • 1
  • R. Dimitrakopoulos
    • 2
  1. 1.Geological Survey of CanadaOttawaCanada
  2. 2.W. H. Bryan Mining Geology Research CentreUniversity of QueenslandBrisbaneAustralia

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