Broad frequency band instantaneous spectral auto-adaptive data fusion based on principal components analysis
Chen Xue-hua1,2, He Zhen-hua1,2, Zhong Wen-li3, Yang Jun4
1. State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China;
2. College of Geophysics, Chengdu University of Technology, Chengdu, Sichuan 610059, China;
3. College of Earth Sciences, Chengdu University of Technology, Chengdu, Sichuan 610059, China;
4. CNOOC Research Center, Beijing 100027, China
Abstract：In order to extract and highlight the primary feature information of reservoirs from a large number of common frequency attribute data generated by instantaneous spectral decomposition,an auto-adaptive data fusion method based on the principal components analysis(PCA) is presented in the paper.The method regards the eigenvalue of principal components after PCA as weighting,which adaptively reflects the contribution rate of each principal component in representing the amount of information of the original data.The method can highlight and reflect the effective information in each instantaneous spectral data more excellently.It achieves the dimensionality reduction and optimization of broad-band instantaneous data sets.The real data processing demonstrates that the method extracts fast and highlights the primary information contained in a large number of instantaneous spectral data set,clearly depicts the geometry and spatial distribution characteristics of reservoirs,and improves the efficiency of data interpretation.
陈学华, 贺振华, 钟文丽, 杨俊. 基于主成分分析的自适应宽频带瞬时谱数据融合[J]. 石油地球物理勘探, 2011, 46(4): 576-580.
Chen Xue-hua, He Zhen-hua, Zhong Wen-li, Yang Jun. Broad frequency band instantaneous spectral auto-adaptive data fusion based on principal components analysis. OGP, 2011, 46(4): 576-580.
