Generalized Gaussian Random Fields using hidden selections
Methodology
2014-02-06 v1
Abstract
We study non-Gaussian random fields constructed by the selection normal distribution, and we term them selection Gaussian random fields. The selection Gaussian random field can capture skewness, multi-modality, and to some extend heavy tails in the marginal distribution. We present a Metropolis-Hastings algorithm for efficient simulation of realizations from the random field, and a numerical algorithm for estimating model parameters by maximum likelihood. The algorithms are demonstrated and evaluated on synthetic cases and on a real seismic data set from the North Sea. In the North Sea data set we are able to reduce the mean square prediction error by 20-40% compared to a Gaussian model, and we obtain more reliable prediction intervals.
Cite
@article{arxiv.1402.1144,
title = {Generalized Gaussian Random Fields using hidden selections},
author = {Kjartan Rimstad and Henning Omre},
journal= {arXiv preprint arXiv:1402.1144},
year = {2014}
}