Implementation of Multivariate Clustering Methods for Characterizing Discontinuities Data from Scanlines and Oriented Boreholes
In geological engineering, discontinuities are typically analyzed by grouping (clustering) them into subsets based on similar orientations, and then characterizing each set in terms of position, spacing, persistence, roughness and other parameters. Multivariate analysis can be used to incorporate some of these other parameters directly into the cluster analysis. The implementation of four methods of cluster analysis that consider orientation, spacing and roughness are described here: nearest neighbor, k-means, fuzzy c-means, and vector quantization. The net result is a better grouping of discontinuities, so that members of a subset might be more uniform in terms of mechanical or hydrological properties. This paper presents the implementation of this analysis in a Windows® based program CYL that also serves as a graphical visualization tool.
W. Zhou and N. H. Maerz, "Implementation of Multivariate Clustering Methods for Characterizing Discontinuities Data from Scanlines and Oriented Boreholes," Computers and Geosciences, vol. 28, no. 7, pp. 827-839, Elsevier, Aug 2002.
The definitive version is available at https://doi.org/10.1016/S0098-3004(01)00111-X
Geosciences and Geological and Petroleum Engineering
National Science Foundation (U.S.)
University of Missouri Research Board
Keywords and Phrases
Discontinuity sets; Discontinuous rock; Euclidean norm; Multivariate cluster analysis algorithms; Visualization tools; Boreholes; Hydrology; Surface roughness; Vector quantization; Clustering; Computers; cluster analysis; engineering geology; orientation
International Standard Serial Number (ISSN)
Article - Journal
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