Open AccessApplication research of attribute fusion technology based on principal component analysis in fracture identification
Author(s) -
Rui Wang,
Qiong Li
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/671/1/012026
Subject(s) - principal component analysis , identification (biology) , fault (geology) , computer science , pattern recognition (psychology) , feature (linguistics) , data mining , artificial intelligence , fusion , seismic attribute , range (aeronautics) , component (thermodynamics) , fracture (geology) , interpretation (philosophy) , engineering , geology , seismology , geotechnical engineering , linguistics , philosophy , botany , physics , aerospace engineering , biology , thermodynamics , programming language
The seismic interpretation and analysis of a single attribute has multiple solutions and limitations, so the article proposes the technical research on the feature identification of fault structure based on the attribute fusion of principal component analysis (PCA). The results indicate that, compared with a single attribute, the integrated seismic attributes obtained by the fusion of the principal component analysis (PCA) method can more clearly reflect the development direction and boundary range of the fault, and the small fractures distributed around it can also be more obvious. The characterization proves that this technology has great application potential in fracture identification.