Conditional simulation of categorical spatial variables using Gibbs sampling of a truncated multivariate normal distribution subject to linear inequality constraints

This paper introduces a method to generate conditional categorical simulations, given an ensemble of partially conditioned (or unconditional) categorical simulations derived from any simulation process. The proposed conditioning method relies on implicit functions (signed distance functions) for rep...

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Veröffentlicht in:Stochastic environmental research and risk assessment Jg. 35; H. 2; S. 457 - 480
Hauptverfasser: Fouedjio, Francky, Scheidt, Celine, Yang, Liang, Wang, Yizheng, Caers, Jef
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021
Springer Nature B.V
Schlagworte:
Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Conditioning
Earth and Environmental Science
Earth Sciences
Environment
Inequality
Linear functions
Math. Appl. in Environmental Science
Normal distribution
Original Paper
Physics
Principal components analysis
Probability Theory and Stochastic Processes
Sampling
Simulation
Statistics for Engineering
Waste Water Technology
Water Management
Water Pollution Control
Gibbs sampler
Conditional simulation
Categorical spatial variable
Implicit function
Principal component analysis
ISSN:1436-3240, 1436-3259
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Zusammenfassung:This paper introduces a method to generate conditional categorical simulations, given an ensemble of partially conditioned (or unconditional) categorical simulations derived from any simulation process. The proposed conditioning method relies on implicit functions (signed distance functions) for representing the categorical spatial variable of interest. Thus, the conditioning problem is reformulated in terms of signed distance functions. The proposed approach combines aspects of principal component analysis and Gibbs sampling to achieve the conditioning of the unconditional categorical realizations to the data. It is applied to synthetic and real-world datasets and compared to the traditional sequential indicator simulation. It appears that the proposed simulation technique is an effective method to generate conditional categorical simulations from a set of unconditional categorical simulations.
Bibliographie:ObjectType-Article-1
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ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-020-01925-7