Broad Frequency Band Instantaneous Spectral Auto-Adaptive Data Fusion Based on Principal Components Analysis
详细信息
- 责任者：Chen Xuehua (State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation ; Chengdu University of Technology ; Chengdu 610059 ; China) ; He Zhenhua
- 刊名：Oil Geophysical Prospecting
- 出版年：2011
- 卷期：46(4)
- 关键词：seismic exploration ; 数据处理 ; data processing
- 页码：p.576-580
- 图表：3 illus., 13 refs.
- 分类号：1600
- 出版序号：1201
- issnNo：ISSN1000-7210
- isbnNo：CN13-1095/TE
摘要
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 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.