Can a Training Image Be a Substitute for a Random Field Model?
Artículo

Publication date
2014Metadata
Show full item record
Cómo citar
Emery, Xavier
Cómo citar
Can a Training Image Be a Substitute for a Random Field Model?
Author
Abstract
In most multiple-point simulation algorithms, all statistical features are
provided by one or several training images (TI) that serve as a substitute for a random
field model. However, because in practice the TI is always of finite size, the
stochastic nature of multiple-point simulation is questionable. This issue is addressed
by considering the case of a sequential simulation algorithm applied to a binary TI
that is a genuine realization of an underlying random field. At each step, the algorithm
uses templates containing the current target point as well as all previously simulated
points. The simulation is validated by checking that all statistical features of
the random field (supported by the simulation domain) are retrieved as an average
over a large number of outcomes. The results are as follows. It is demonstrated that
multiple-point simulation performs well whenever the TI is a complete (infinitely
large) realization of a stationary, ergodic random field. As soon as the TI is restricted
to a limited domain, the statistical features cannot be obtained exactly, but integral
range techniques make it possible to predict how much the TI should be extended
to approximate them up to a prespecified precision. Moreover, one can take advantage
of extending the TI to reduce the number of disruptions in the execution of the
algorithm, which arise when no conditioning template can be found in the TI.
General note
Artículo de publicación ISI
Identifier
URI: https://repositorio.uchile.cl/handle/2250/126568
DOI: DOI: 10.1007/s11004-013-9492-z
Quote Item
Math Geosci (2014) 46:133–147
Collections