Investigating the validity of conventional joint set clustering methods

Up to 10 properties of joints can be recorded in the field, yet only two (dip and dip direction) are commonly used to identify joint sets. This paper investigates some of the shortcomings of commonly employed methods for joint set clustering, based on an analysis of synthetic and field data. First, eight synthetic joint sets were generated using a normal distribution of joint orientations. Each joint was defined in terms of four properties (dip, dip direction, infilling material and infilling percentage). A Parzen classifier was used to confirm the importance of using all the joint properties in identifying the joint sets. To investigate the generalization ability of this approach, the analysis was extended to 178 joints measured in the field, with seven properties available for each joint. Joints were clustered based on rose diagrams, stereonets, and K-means clustering methods, yielding three, five, and seven joint sets, respectively. Calculation of the coefficient of variation and principal component analysis (PCA) of joint properties resulted in an improvement in clustering, provided that a large number of joint properties are considered.

Research Highlights
► Introducing a novel approach in joints clustering backed up with clear rationale.
► Paper used both clustering and classification methods.
► Paper highlighted the importance of fracture properties, in the clustering process.
► Paper clustered joints in seven dimensional spaces (seven joint properties).
► About 50% of joints variability is lost when utilizing only dip and dip direction.