Abstract
Multiple categorical variables such as mineralization zones, alteration zones, and lithology are often available for geostatistical modeling. Each categorical variable has a number of possible categorical outcomes. The current approach for numerical modeling of categorical variables is to either combine the categorical variables or to model them independently. The collapse of multiple categorical variables into a single variable with all combinations is impractical due to the large number of combinations. In some cases, lumping categorical variables is justified in terms of stationary domains; however, this decision is often due to the limitations of existing techniques. The independent modeling of each categorical variable will fail to reproduce the collocated joint categorical relationships. A methodology for the multivariate modeling of categorical variables utilizing the hierarchical truncated pluri-Gaussian approach is developed and illustrated with the Swiss Jura data set. The multivariate approach allows for improved reproduction of multivariate relationships between categorical variables.
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(modified from Garza et al. 2001)
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Silva, D., Deutsch, C. Multivariate Categorical Modeling with Hierarchical Truncated Pluri-Gaussian Simulation. Math Geosci 51, 527–552 (2019). https://doi.org/10.1007/s11004-018-09782-5
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DOI: https://doi.org/10.1007/s11004-018-09782-5