Stochastic Interpolation of Spatial Random Fields by BF/MCF-ISM
Publication: Journal of Engineering Mechanics
Volume 134, Issue 2
Abstract
In the past, interpolation of random fields was successfully treated by Kriging methods for Gaussian fields, and by conditional simulation techniques for a class of non-Gaussian translation fields. Recently, bootstrap filter/Monte Carlo filter (BF/MCF) is extensively used for interpolation of general non-Gaussian fields. However, while BF/MCF is a versatile tool to interpolate non-Gaussian fields, that is an algorithm of generating a set of sample realizations of both a predicted state vector and a filtered state vector, the computational cost is expensive due to the required sample size. In order to reduce the required sample size, an importance sampling function derived from the updating theory of Gaussian fields is applied to the ordinary BF/MCF. Interpolation of spatial fields is first demonstrated by using numerically simulated data, and the BF/MCF incorporated with importance sampling technique (BF/MCF-ISM) for the state estimation of conditional non-Gaussian fields is performed with respect to its efficiency in variance reduction.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 134 • Issue 2 • February 2008
Pages: 198 - 205
Copyright
© 2008 ASCE.
History
Received: Apr 18, 2006
Accepted: May 3, 2007
Published online: Feb 1, 2008
Published in print: Feb 2008
Notes
Note. Associate Editor: Arvid Naess
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