Fault recognition based on affinity propagation clustering and principal component analysis
Chen Lei1, Xiao Chuangbai2, Yu Jing2, Wang Zhenli1, Li Xueliang1
1. Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;
2. College of Computer Science and Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Abstract：As conventional fault recognition methods have some difficulties such as low accuracy,time-consuming,and non-quantified results,we propose a new method based on affinity propagation clustering and principal component analysis (PCA) in this paper.At first,discontinuous points of horizons are located by the connected component labeling method.Then,the discontinuous points are clustered by the affinity propagation clustering algorithm and a fault is determined by a type of discontinuous point cluster.In this way,both the number of faults and the centers of all the cluster types are obtained.At last,the principal directions of the discontinuous points of all the cluster types are determined by the PCA.A direct line along the principal direction which passes through a cluster center is considered as a fault.The proposed algorithm and conventional methods are tested on model and real seismic data,and the rationality of the proposed method is validated by the peak signal-to-noise ratio,mean square error,time consumption,and coincidence rate for the number of faults.In addition,fault quantitative interpretation achieved by the proposed algorithm possesses great practical significance in the seismic exploration.
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