Abstract
The sequential indicator algorithm is a widespread geostatistical simulation technique that relies on indicator (co)kriging and is applicable to a wide range of datasets. However, such algorithm comes up against several limitations that are often misunderstood. This work aims at highlighting these limitations, by examining what are the conditions for the realizations to reproduce the input parameters (indicator means and correlograms) and what happens with the other parameters (other two-point or multiple-point statistics). Several types of random functions are contemplated, namely: the mosaic model, random sets, models defined by multiple indicators and isofactorial models. In each case, the conditions for the sequential algorithm to honor the model parameters are sought after. Concurrently, the properties of the multivariate distributions are identified and some conceptual impediments are emphasized. In particular, the prior multiple-point statistics are shown to depend on external factors such as the total number of simulated nodes and the number and locations of the samples. As a consequence, common applications such as a flow simulation or a change of support on the realizations may lead to hazardous interpretations.
Similar content being viewed by others
Acknowledgments.
The author would like to acknowledge the reviewers for their helpful comments and the sponsoring by Codelco-Chile for supporting this research.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Emery, X. Properties and limitations of sequential indicator simulation. Stochastic Environmental Research and Risk Assessment 18, 414–424 (2004). https://doi.org/10.1007/s00477-004-0213-5
Issue Date:
DOI: https://doi.org/10.1007/s00477-004-0213-5