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arXiv · 1402.1144

Generalized Gaussian Random Fields using hidden selections

Kjartan Rimstad·Henning Omre
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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.

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Kjartan Rimstad, Henning Omre. 2014-02-05. Generalized Gaussian Random Fields using hidden selections. https://arxiv.org/abs/1402.1144

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