Abstract:Multi-attributes clustering is an important way for drawing underground geologic features from a large number of seismic attributes.For improving the effectiveness of the attributes clustering,this paper introduces quantum Monte Carlo method with stronger optimization ability into the clustering to adjust the inherent category number of data dynamically according to the characteristics of the data structure.By increasing the correlation analysis in the process of clustering for estimating each attribute weight,seismic attributes which are sensitive to geological features can be highlighted.The proposed variable scale method can show details together with macro common attribute characteristics to reduce the influence of attribute cross information.Examples show that the method proposed in this paper could be very good at mining the inherent characteristics of data to improve reservoir prediction accuracy.
魏超, 郑晓东, 李劲松, 李萌. 基于量子蒙特卡罗的地震多属性聚类方法[J]. 石油地球物理勘探, 2012, 47(5): 747-753.
Wei Chao, Zheng Xiao-dong, Li Jin-song, Li Meng. The seismic multi-attribute clustering method based on quantum Monte Carlo method. OGP, 2012, 47(5): 747-753.