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A multiple training image approach for spatial modeling of geologic domains

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Publication:887591
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DOI10.1007/s11004-014-9543-0zbMath1323.86035OpenAlexW2068825924MaRDI QIDQ887591

Daniel A. Silva, Clayton V. Deutsch

Publication date: 26 October 2015

Published in: Mathematical Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11004-014-9543-0

zbMATH Keywords

entropyMPSgeostatisticstraining imagelinear opinion poolgeologic domains


Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Geostatistics (86A32) Geological problems (86A60)



Uses Software

  • GSLIB


Cites Work

  • Unnamed Item
  • An improved parallel multiple-point algorithm using a list approach
  • High-order statistics of spatial random fields: Exploring spatial cumulants for modeling complex non-Gaussian and non-linear phenomena
  • The necessity of a multiple-point prior model
  • Multiple-point simulations constrained by continuous auxiliary data
  • The nu expression for probabilistic data integration
  • Combining knowledge from diverse sources: An alternative to traditional data independence hypotheses
  • Probability aggregation methods in geoscience
  • The Opinion Pool
  • A Bayesian/maximum-entropy view to the spatial estimation problem
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