Multivariate Clustering Analysis of Discontinuity Data: Implementation and Applications

This paper presents the results of ongoing research on the characterization of rock mass structure from discontinuity data. Multivariate clustering analysis represents a relatively recent development in characterizing the structure of rock masses. Multivariate clustering allows characterization of discontinuities into subsets according to multiple parameters, such as orientation, spacing, and roughness, where, rather than considering one variable at a time, a number of parameters can be treated simultaneously, so that the interactions between parameters are taken into account. The comprehensive algorithm has been developed into a software package called CYL. It enables fully automated multivariate clustering analysis and offers various visualization tools, such as a three-dimensional stereonet, a stereoscopic view, and a statistical table. In this paper we focus on the implementation of the algorithms and the application of this method to field data, both from oriented core and mapped road cut discontinuity data.