The necessity of a multiple-point prior model

DOI10.1007/S11004-006-9031-2zbMath1130.86312OpenAlexW1989726841MaRDI QIDQ883167

Tuanfeng Zhang, Andre G. Journel

Publication date: 31 May 2007

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

Full work available at URL: https://doi.org/10.1007/s11004-006-9031-2

Mathematics Subject Classification ID

Geostatistics (86A32)

Related Items (11)

A multiple training image approach for spatial modeling of geologic domains ⋮ Improving the pattern reproducibility of multiple-point-based prior models using frequency matching ⋮ High-order sequential simulation via statistical learning in reproducing kernel Hilbert space ⋮ Exploiting transformation-domain sparsity for fast query in multiple-point geostatistics ⋮ Extrapolating the fractal characteristics of an image using scale-invariant multiple-point statistics ⋮ Construction of binary multi-grid Markov random field prior models from training images ⋮ Reconstruction of incomplete data sets or images using direct sampling ⋮ An improved parallel multiple-point algorithm using a list approach ⋮ Linear waveform tomography inversion using machine learning algorithms ⋮ Special issue on 20 years of multiple-point statistics. I. ⋮ Can a training image be a substitute for a random field model?

Uses Software

• GSLIB

Cites Work

• Filter-based classification of training image patterns for spatial simulation
• Spatial connectivity: from variograms to multiple-point measures
• Conditional simulation of complex geological structures using multiple-point statistics

This page was built for publication: The necessity of a multiple-point prior model