ارايه روشي جديد براي رده بندي درز ها با رويكرد بهينه رده بندي كننده بيزين

A New Method for Joint Set Classification Based on Bayesian Optimum Classifier

Joint study is one of the primary jobs in many geological, mining, geotechnical and petroleum exploration projects. Up to 10 features of joints are gathered during each field survey, while only two of them (dip and dip direction) are normally used to classify these complex features. This paper proposes a new method for joint set classification which can use more than two surveyed features. A synthetic set of 8 joint set, each joint defined with 4 features (dip, dip direction, type of infilling and amount of infilling), created in a way that with two features (dip and dip direction) sets could not be differentiated. Necessary program developed to use Bayesian classifier to sort 8 synthetic joint sets in 4D space. Present study showed that all 8 sets can be successfully differentiated by using Bayesian method.