application of gustafson-kessel clustering algorithm for detecting fault through seismic attributes

in this paper an application of gustafson-kessel clustering algorithm is presented to create a fault detection map (fdm). five post-stack seismic attributes are extracted from a desired seismic time slice related to 3d seismic data of a gas field located in southwest of iran. to find the optimal cluster numbers, two frequently used clustering validity measures, i.e. sc and xb, are used and then the studied area were divided into regions which have high possibility for exploring faults. the proposed method helps expert geologists to enhance their interpretations in identifying subtle faults and provides an effective fdm generating approach.