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Fault Detection Based on AP Clustering and PCA

文献类型：期刊论文

作者Chen, Lei1; Xiao, Chuangbai1; Yu, Jing1; Wang, Zhenli2
刊名INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
出版日期2018-02-01
卷号32期号:2
关键词Fault Detection Connected Component Ap Clustering Pca
ISSN号0218-0014
DOI10.1142/S0218001418500015
文献子类Article
英文摘要To improve the accuracy, reduce the time consumption and obtain the number of faults, a fault detection method based on AP (affinity propagation) clustering and PCA (principal component analysis) was proposed. Firstly, discontinuous points in seismic horizons were searched out by the connected component labeling method. Secondly, the AP clustering algorithm was used to cluster the discontinuous points and the points of the same cluster were used to determine a fault, meanwhile, the faults existing in a seismic section were quantified. Finally, the PCA was adopted to calculate the principal direction of the discontinuous points contained in the same cluster. As a result, the corresponding cluster center and the principal direction determined a straight line, and the part that intercepted by the clustered edge was the fault we wanted. In the proposed method, the time consumption of correlation calculation of the traditional method was reduced; the computing work was simplified and the number of the faults in the seismic section was obtained. To confirm the feasibility and advancement of the proposed method, comparative experiments were done on the seismic model data and the real seismic section. The results show that the accuracy of the proposed method was better and the time cost was greatly reduced.
WOS关键词ALGORITHM ; TRACKING
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000415083900001
出版者WORLD SCIENTIFIC PUBL CO PTE LTD
资助机构National Natural Science Foundation of China(61501008) ; Beijing Natural Science Foundation(4162007) ; National Natural Science Foundation of China(61501008) ; Beijing Natural Science Foundation(4162007) ; National Natural Science Foundation of China(61501008) ; Beijing Natural Science Foundation(4162007) ; National Natural Science Foundation of China(61501008) ; Beijing Natural Science Foundation(4162007)
源URL[http://ir.iggcas.ac.cn/handle/132A11/62466]  
专题地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者Chen, Lei
作者单位1.Beijing Univ Technol, Coll Comp Sci & Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Lei,Xiao, Chuangbai,Yu, Jing,et al. Fault Detection Based on AP Clustering and PCA[J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,2018,32(2).
APA Chen, Lei,Xiao, Chuangbai,Yu, Jing,&Wang, Zhenli.(2018).Fault Detection Based on AP Clustering and PCA.INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,32(2).
MLA Chen, Lei,et al."Fault Detection Based on AP Clustering and PCA".INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 32.2(2018).

入库方式： OAI收割

来源：地质与地球物理研究所

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