Bayesian Spatial Modelling of Gamma Ray Count Data

被引:0
作者
Daniela Leonte
David J. Nott
机构
[1] The University of New South Wales,School of Safety Science
[2] The University of New South Wales,Department of Statistics
来源
Mathematical Geology | 2006年 / 38卷
关键词
Bayesian inference; gamma ray data; Markov chain Monte Carlo; spatial partition modelling; Voronoi tessellations;
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学科分类号
摘要
Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.
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页码:135 / 154
页数:19
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