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GLCM-based anisotropy estimation — the influence of computation parameters on the resultsNormal access

Authors: Christoph Georg Eichkitz and Johannes Amtmann
Journal name: First Break
Issue: Vol 36, No 5, May 2018 pp. 47 - 52
Language: English
Info: Article, PDF ( 2.54Mb )
Price: € 30

The grey level co-occurrence matrix (GLCM) is a second-order statistical texture classification method initially described by Haralick et al., (1973). Typically, two neighbouring images are compared by using a moving analysis window to construct a 2D GLCM. This is used subsequently in the calculation of GLCMbased attributes. Common applications of GLCM attributes include classification of satellite images (Franklin et al., 2001; Tsai et al., 2007) and images based on magnetic resonance or computed tomography (Kovalev et al., 2001; Zizzari et al., 2011). GLCM has played a minor role in seismic interpretation, but within the last 20 years several authors have used the GLCM method to interpret channels systems (Eichkitz et al., 2013, 2014, 2015a, 2015b, 2016; West et al., 2002; Gao, 2007, 2011; de Matos et al., 2011), sedimentary facies (Di and Gao, 2017; Eichkitz, et al., 2012; Chopra and Alexeev, 2005, 2006a, 2006b; Yenugu et al., 2010; Wang et al., 2016), salt bodies (Gao, 2003), and fractures (Eichkitz et al., 2015c, 2016; Schneider et al., 2016). Most of these studies focus on direct extraction of information from GLCM-based attributes. An alternative approach is to compare information from directional GLCM-based attribute computations. GLCM attributes calculated in different directions will differ slightly, and can be used to investigate anisotropy (Eichkitz et al., 2016). In this paper we focus on the main parameters used in GLCM calculations and their effect on GLCM-based anisotropy estimations. The parameters studied include the number of grey levels, the type of grey–level transformation, the size of the analysis window, and the type of GLCM-based attribute. Only one parameter at a time was altered, while other parameters were held at a default value. Results were compared visually to find recommendations for parameters best suited to different interpretation purposes. Our workflow for GLCM-based anisotropy estimation was applied to data on the fractured reservoir of the Tensleep Formation at Teapot Dome, Wyoming.

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