Generalized Gaussian Random Fields using hidden selections
arXiv (Cornell University)(2014)
摘要
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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关键词
seismic inversion,spatial statistics
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