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Ensemble methods work with continuous Gaussian distributed parameters and cannot directly handle non-Gaussian or discrete ones. However, it is straightforward to represent non-Gaussian parameters with an underlying Gaussian representation followed by a transformation into the non-Gaussian parameters. We can then use the ensemble methods without violating the Gaussian assumption, but at the disadvantage of introducing additional nonlinearity through the nonlinear transformation. This chapter will discuss the formulation of the TruncatedPluri-Gaussiantruncated Pluri-Gaussian SimulationTruncated Pluri-Gaussian Simulation (TPS) (TPS) and Adaptive Pluri-Gaussian Pluri-Gaussianadaptive SimulationPluri-Gaussiansimulation (APSAPS) methodsPluri-Gaussianmethod for modeling categorical facies variables.

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Non-gaussian and Categorical Variables

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Geir Evensen,
  • Dean S. Oliver,
  • Remus G. Hanea

摘要

Ensemble methods work with continuous Gaussian distributed parameters and cannot directly handle non-Gaussian or discrete ones. However, it is straightforward to represent non-Gaussian parameters with an underlying Gaussian representation followed by a transformation into the non-Gaussian parameters. We can then use the ensemble methods without violating the Gaussian assumption, but at the disadvantage of introducing additional nonlinearity through the nonlinear transformation. This chapter will discuss the formulation of the TruncatedPluri-Gaussiantruncated Pluri-Gaussian SimulationTruncated Pluri-Gaussian Simulation (TPS) (TPS) and Adaptive Pluri-Gaussian Pluri-Gaussianadaptive SimulationPluri-Gaussiansimulation (APSAPS) methodsPluri-Gaussianmethod for modeling categorical facies variables.