| 基于主成分分析与K-L变换的双重属性优化方法 |
| |
| 引用本文： | 赵加凡,陈小宏.基于主成分分析与K-L变换的双重属性优化方法[J].物探与化探,2005,29(3):253-256. |
| |
| 作者姓名： | 赵加凡 陈小宏 |
| |
| 作者单位： | 石油大学,CNPC物探重点实验室,北京,102249 |
| |
| 摘 要： | 利用主成分分析客观赋权原理计算地震属性在预测目标参数时贡献率的大小,通过去除权重系数较小的属性参数,实现了地震属性的敏感性分析,建立储层参数与有效属性之间的匹配关联;在此基础上,利用K-L变换将属性样本空间的高维属性映射为低维属性,去除了属性之间的相关性,有效地解决了属性组合的优化问题,表明了主成分分析和K-L变换相结合的属性双重优化方法克服了单纯使用每种方法时的局限性,充分发挥了各自的优点,有助于属性分析、关联以及组合优化问题的解决,提高了地震储层参数预测的运算速度和精度。
|
| 关 键 词： | 地震数据处理 主成分分析 K-L变换 神经网络 属性组合与优化 |
| 文章编号： | 1000-8918(2005)03-0253-04 |
| 收稿时间： | 2004-03-04 |
DUAL OPTIMIZATION OF SEISMIC ATTRIBUTES BASED ON PRINCIPAL COMPONENT ANALYSIS AND K-L TRANSFORM |
| |
| ZHAO Jia-fan,CHEN Xiao-hong.DUAL OPTIMIZATION OF SEISMIC ATTRIBUTES BASED ON PRINCIPAL COMPONENT ANALYSIS AND K-L TRANSFORM[J].Geophysical and Geochemical Exploration,2005,29(3):253-256. |
| |
| Authors: | ZHAO Jia-fan CHEN Xiao-hong |
| |
| Institution: | Key lab of Geophysical Exploration under CNPC, University of Petroleum , Beijing 102249, China |
| |
| Abstract: | The deciding weight theory of Principal Components Analysis is used to compute contribution values of seismic attributes to forecasting parameters, and the attributes sensitivity analysis is solved by getting rid of these attributes with lesser weight coefficient. In this way, the association between reservoir parameters and attributes is established. By means of K-L transform, higher dimensional attributes are mapped to lower dimensional ones while correlation between attributes is eliminated, thus completing the optimization of attributes. In this paper, the target parameters are predicted by BP neural network. The application shows that the dual optimization method combining Principal Components analysis with K-L transform overcomes the limitation of either method and at the same time possesses their respective merits. In short, the method gives a satisfactory solution to the sensitivity analysis, association, and combination optimization of seismic attributes, and eventually improves the precision, speed, and efficiency of reservoir prediction. |
| |
| Keywords: | principal components analysis K-L transform neural network attributes combination attributes optimization |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《物探与化探》浏览原始摘要信息 |
| 点击此处可从《物探与化探》下载免费的PDF全文 |
|
