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Journal of the American Statistical Association Volume 96, 2001 - Issue 453
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Primary Article

Fitting Mixtures of Kent Distributions to Aid in Joint Set Identification

David Peel David Peel is Postdoctoral Research Fellow and Geoffrey J. McLachlan is Professor, Department of Mathematics, University of Queensland, St. Lucia, Queensland 4072, Australia (E-mails: . William J. Whiten is a Professorial Research Fellow, Julius Kruttschnitt Mineral Research Centre, University of Queensland, St. Lucia, Queensland 4072, Australia . The authors thank Professor E. T. Brown, Dr. Gideon Chitombo, and other members of the mining research group at the JKMRC for providing the data used in this article and information regarding the application.
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William J Whiten David Peel is Postdoctoral Research Fellow and Geoffrey J. McLachlan is Professor, Department of Mathematics, University of Queensland, St. Lucia, Queensland 4072, Australia (E-mails: . William J. Whiten is a Professorial Research Fellow, Julius Kruttschnitt Mineral Research Centre, University of Queensland, St. Lucia, Queensland 4072, Australia . The authors thank Professor E. T. Brown, Dr. Gideon Chitombo, and other members of the mining research group at the JKMRC for providing the data used in this article and information regarding the application.
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Geoffrey J McLachlan David Peel is Postdoctoral Research Fellow and Geoffrey J. McLachlan is Professor, Department of Mathematics, University of Queensland, St. Lucia, Queensland 4072, Australia (E-mails: . William J. Whiten is a Professorial Research Fellow, Julius Kruttschnitt Mineral Research Centre, University of Queensland, St. Lucia, Queensland 4072, Australia . The authors thank Professor E. T. Brown, Dr. Gideon Chitombo, and other members of the mining research group at the JKMRC for providing the data used in this article and information regarding the application.
Pages 56-63 | Published online: 31 Dec 2011
 
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Abstract

When examining a rock mass, joint sets and their orientations can play a significant role with regard to how the rock mass will behave. To identify joint sets present in the rock mass, the orientation of individual fracture planes can be measured on exposed rock faces and the resulting data can be examined for heterogeneity. In this article, the expectation–maximization algorithm is used to fit mixtures of Kent component distributions to the fracture data to aid in the identification of joint sets. An additional uniform component is also included in the model to accommodate the noise present in the data.

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