摘要
成像测井由于其能够以图像的形式直观表示地层的岩性与结构特征,已成为测井领域的研究热点之一.如何利用图像处理、模式识别等相关理论方法对成像测井图像进行较为精确的定量评价和解释是研究的核心.首先从成像测井的研究背景及现状出发,详细比较和分析了纹理分析的各种算法.通过对灰度共生矩阵法、LBP算法、Gabor变换、小波变换、Contourlet变换等算法进行比较,给出成像测井图像分类过程中特征提取的参考建议.在此基础上,结合测井图像的模式特点,提出了一个基于纹理特征的成像测井图像分类系统模型.最后总结了该领域所面临的问题及未来的研究发展趋势.
Imaging well logging can easily and effectively determine the existing locations of the reservoirs, because of its ability of showing the stratal lithologies and geometric changes in the form of image. Recently, imaging log is used widely for its higher discernibility and has become a research hotspot in the field of well logging technology. How to fully utilize image processing, pattern recognition and other related theoretical methods for both more precise quantitative evaluation of imaging logging and the interpretation of images is the focus. Texture analysis plays an important role in the field of computer vision and pattern recognition. This paper surveys the research background and current situation of the imaging well logging, and then reviews most existing typical algorithms for texture analysis. It focuses on the grey level co-occurrence matrices （GLCM） algorithm, local binary patterns algorithm （LBP）, Gabor transform, wavelet transform, as well as Contourlet transform, and analyzes their respective pros and cons. Based on this, and considering the features of the logging images, this paper gives a method for the classification of the well logging images, and proposes a system model of logging image recognition and classification. The problems, prospects for future development and suggestions for further research works are put forward at the end of the paper.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2013年第6期1335-1348,共14页
Journal of Computer Research and Development
基金
国家自然科学基金项目(60873158)
国家"九七三"重点基础研究发展计划基金项目(2010CB327902)
关键词
成像测井
纹理分析
特征提取
模式识别
综述
imaging well logging texture analysis feature extraction pattern recognition review
