二维高斯迭代平滑滤波曲率属性及其应用  

Curvature attribute of two-dimensional Gaussian iterated smoothing filtering and applications

作　　者：伍鹏[1,2] 贺振华[1,2] 陈学华[1,2] 焦琛[1] 

机构地区：[1]成都理工大学信息工程学院,成都610059 [2]成都理工大学油气藏地质及开发工程国家重点实验室,成都610059

出　　处：《地球物理学进展》2010年第6期2144-2149,共6页Progress in Geophysics

基　　金：国家高技术研究发展计划("八六三"计划)(2006AA0AA102-12);国家自然科学基金(40774064)联合资助项目

摘　　要：基于二维高斯迭代平滑滤波的曲率属性的应用,实现对裂缝、断层、弯曲和褶皱等地质构造的有效识别.本文介绍了曲率属性的基本理论,分析了未滤波和经过中值滤波、高斯滤波在处理实际资料中的局限性,提出了一种基于二维高斯迭代平滑滤波来表现曲率属性的方法:对原始数据体进行中值滤波和二维迭代平滑高斯滤波的处理,将处理后的地震资料进行曲率属性的求取,然后对经过各种滤波的地震属性的成图效果进行比较,分析异同,得出结论表明基于二维高斯迭代平滑滤波的曲率属性在高分辨率构造精细识别尤其是隐伏断层和裂缝的判别、构造体的几何特征描述上有着良好的应用效果.Based on the application of the curvature attribute of two-dimensional Gaussian iterated smoothing filterings, we can realize the effective identification of geological structures,such as fissure, faults, bends and folds. This article describes the basic theory of the curvature attribute, analyzes the limitations of using no filtering and after median filtering, Gaussian filtering dealing with real information, proposes a method of using two-dimensional Gaussian iterated smoothing filtering to express the curvature attribute. It deals the original data volume with the median filtering and two-dimensional Gaussian iterated smoothing filtering, calculates the curvature attribute from the processed seismic data. Then it compares the effects of seismic attribute mapping through a variety of filtering and analysis of the similarities and differences. We have concluded that it has a good application effect for high-resolution structure and fine identification, especially in the discrimination of buried faults and cracks and description of geometric features.

关 键 词：二维迭代高斯滤波 中值滤波 曲率属性 构造识别 

分 类 号：P631[天文地球—地质矿产勘探]