Anisotropy estimation based on the grey level co-occurrence matrix (GLCM)

A nisotropy refers to directional properties. In geophysics, we often refer to seismic anisotropy, the dependence of velocity on direction or upon angle (e.g., Crampin 1981, 1985; Lynn and Thomsen, 1990; Willis et al., 1986, Martin and Davis, 1987; Thomsen, 1986; Alkhalifah and Tsvankin, 1995). Variation in seismic velocity with direction may reflect lateral changes in facies, the presence of faults or fractures, or differences in pore fillings, among many factors that may influence velocity. In principal, seismic data can be used to estimate volumetric azimuthal anisotropy (Simon, 2005). In this work we focus on the application of textural attributes to estimate volumetric anisotropy. The grey level co-occurrence matrix (GLCM), initially described by Haralick et al. (1973) as a tool for image classification, is a measure of how often different combinations of pixel brightness values occur in an image. This method has widely been used for classification of satellite images (Franklin, et al., 2001; Tsai, et al., 2007), sea-ice images (Soh and Tsatsoulis, 1999; Maillard et al., 2005), and magnetic resonance and computed tomography images (Kovalev et al., 2001; Zizzari et al., 2011). This methodology can also be applied to seismic data to describe facies, reservoir properties, and fractures (Vinther et al., 1996; Gao, 1999, 2003, 2007, 2008a, 2008b, 2009, 2011; West et al., 2002; Chopra and Alexeev, 2005, 2006a, 2006b; Yenugu et al., 2010; de Matos et al., 2011; Eichkitz and Amtmann et al., 2012b, 2013, 2014, Eichkitz and de Groot et al., 2014; Eichkitz et al., 2015a, 2015b). GLCM–based attributes can be calculated in different directions, yielding an array of radial responses. By comparing these different results it is possible to determine anisotropy in the seismic data. Here, directional GLCM–based attributes are used for the description of channel structures and for the interpretation of fractured reservoirs.