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Multivariate Bayesian Classification Models: Application to the Optimal Selection of Prospecting Areas and Exploration Targets

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Statistical Models for Optimizing Mineral Exploration

Abstract

The problem of decision-making under uncertainty assumes a critical importance when costly drilling decisions need to be made, particularly in petroleum exploration. We find ourselves in two types of situations: (i) Control data are available in well-prospected areas with similar geological make-up, or (ii) no control locations are available. The second situation is increasingly common, as explorationists have to deal with more remote and little-known regions.

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Authors and Affiliations

  1. University of Guam, Mangilao, Guam

    T. K. Wignall

  2. Geostatistics, Eden, N.S.W, Australia

    J. De Geoffroy (Consultant)

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  1. T. K. Wignall
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  2. J. De Geoffroy
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© 1987 Plenum Press, New York

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Wignall, T.K., De Geoffroy, J. (1987). Multivariate Bayesian Classification Models: Application to the Optimal Selection of Prospecting Areas and Exploration Targets. In: Statistical Models for Optimizing Mineral Exploration. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1861-3_6

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  • DOI: https://doi.org/10.1007/978-1-4613-1861-3_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-9038-4

  • Online ISBN: 978-1-4613-1861-3

  • eBook Packages: Springer Book Archive

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