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A Bayesian statistical model for end member analysis of sediment geochemistry, incorporating spatial dependences
Authors:Mark J. Palmer   Grant B. Douglas
Affiliation:Commonwealth Scientific and Industrial Research Organization Mathematical and Information Sciences, Wembley, Australia;
Commonwealth Scientific and Industrial Research Organization Land and Water, Wembley, Australia
Abstract:Summary.  An important problem in the management of water supplies is identifying the sources of sediment. The paper develops a Bayesian approach, utilizing an end member model, to estimate the proportion of various sources of sediments in samples taken from a dam. This approach not only allows for the incorporation of prior knowledge about the geochemical compositions of the sources (or end members) but also allows for correlation between spatially contiguous samples and the prediction of the sediment's composition at unsampled locations. Sediments that were sampled from the North Pine Dam in south-east Queensland, Australia, are analysed to illustrate the approach.
Keywords:Compositional data    End member model    Geochemistry    Markov chain Monte Carlo methods    Sediments    Spatial prediction
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