Отправлено: 08.09.08 09:20. Заголовок: The segmentation of FMI image based on 2-D dyadic wavelet transform

The segmentation of FMI image based on 2-D dyadic wavelet transform
Applied Geophysics
Chinese Geophysical Society
ISSN 1672-7975 (Print) 1993-0658 (Online)
Volume 2, Number 2 / Июнь 2005 г.
Technical Articles
DOI 10.1007/s11770-005-0039-z
pp. 89-93
Technical Articles
The segmentation of FMI image based on 2-D dyadic wavelet transform
Liu Rui-Lin 1, Wu Yue-Qi 2, Liu Jian-Hua 2 and Ma Yong 2

(1) College of Geophysics and Petroleum resource, Yangtze University, 434023 Jingzhou, Hubei, China
(2) Institute of Engineering and Technology of Northwest Petroleum bureau, CNSPC, 830011 Urumqi, China

Received: 10 November 2004 Revised: 29 December 2004

Abstract A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get images of pores, vugs and fractures. A segmentation method based on the dyadic wavelet transform in 2-D is introduced in this paper. The first step is to find all the edge pixels of the FMI image using the 2-D wavelet transform. The second step is to calculate a segmentation threshold based on the average value of the edge pixels. Field data processing examples show that sub-images of vugs and fractures can be correctly separated from original FMI data continuously and automatically along the depth axis. The image segmentation lays the foundation for in-situ parameter calculation.
Keywords FMI image - wavelet transform - image segmentation - carbonate - fractures - and vugs

The research was supported by the Fifteenth National Scientific and Technological Project (2001-BA605A-03-02).
First author
Liu Ruilin, Ph D, earned his graduate degree in Oceanic Geology and Geophysics from Tongji University in 1997. He is now a professor in Changjiang University and participates the research on several reservoir prediction and image logging data processing.

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References
Crane, B., A Simplified Approach to Image Processing, Prentice Hall PTR, 1997.

Luthi, S. M., And Souhaite, P., 1990, Fracture Aperture from Electrical Borehole Scans, Geophysics,55(7).

Liu, Rui-Lin, Zhou, Yun-Cai et al. 2000, An Application of Dyadic Wavelet Transform in One Dimension to The Segmentation of FMI Images(in Chinese), Proceeding of reflection seismology, Shanghai: Tongji university Press.

Mallat, S., 1989, Multifrequency Channel Decomposition of Image with Wavelet Models: IEEE Trans., ASSP, 39(12).

Mallat S., and Hwang W. L, 1992, Singularity Detection and Processing with Wavelets: IEEE Trans. Information Theory, 38(2).

Mallat S., Zhong S., 1992, Characterization of Signals from Multiscales Edges: IEEE Trans PAMI,14(7).

Pal H.R. and Pal S. K., 1993, A Review on Image Segmentation Techniques, Pattern Recognition, 26(9).

Parker J. R., 1996, Algorithms for Image Processing and Computer Vision, John Wiley &Sun, Inc,.