Analyzing nonstationary spatial data using piecewise Gaussian processes

Journal article

In many problems in geostatistics the response variable of interest is strongly related to the underlying geology of the spatial location. In these situations there is often little correlation in the responses found in different rock strata, so the underlying covariance structure shows sharp changes at the boundaries of the rock types. Conventional stationary and nonstationary spatial methods are inappropriate, because they typically assume that the covariance between points is a smooth function of distance. In this article we propose a generic method for the analysis of spatial data with sharp changes in the underlying covariance structure. Our method works by automatically decomposing the spatial domain into disjoint regions within which the process is assumed to be stationary, but the data are assumed independent across regions. Uncertainty in the number of disjoint regions, their shapes, and the model within regions is dealt with in a fully Bayesian fashion. We illustrate our approach on a previously unpublished dataset relating to soil permeability of the Schneider Buda oil field in Wood County, Texas. © 2005 American Statistical Association.

Authors: Kim, H; Mallick, B; Holmes, C

• Publication status: Published
• Journal: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• Volume: 100
• Issue: 470
• Pages: 653-668
• Publication date: 2005-06-01
• DOI: 10.1098/016214504000002014
• EISSN: 1537-274X
• ISSN: 0162-1459
• Language: English
• Keywords: Voronoi tessellation; model averaging; reversible-jump Markov chain Monte Carlo; kriging; Bayes factor