English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The truncated plurigaussian model is often used to simulate the spatialdistribution of random categorical variables such as geological facies. Theproblems addressed in this paper are the estimation of parameters of thetruncation map for the truncated plurigaussian model. Unlike standardtruncation maps, in this paper a colored Voronoi tessellation with number ofnodes, locations of nodes, and category associated with each node all treatedas unknowns in the optimization. Parameters were adjusted to match categoricalbivariate unit-lag probabilities, which were obtained from a larger patternjoint distribution estimates from the Bayesian maximum-entropy approachconditioned to the unit-lag probabilities. The distribution of categoricalvariables generated from the estimated truncation map was close to the targetunit-lag bivariate probabilities. The validation of the predictive performanceof the model is evaluated using scoring rules, and conditioning of the latentGaussian fields to log-data is generalized for the case when the truncatedbigaussian model is governed by a colored Voronoi tessellation of thetruncation map.

Truncation map estimation based on bivariate probabilities and validation for the truncated plurigaussian model