In recent years, 3D volumetric attributes have gained wide acceptance by geosciences interpreters. The early introduction of single-trace complex trace attributes was quickly followed by seismic sequence attribute mapping workflows. 3D geometric attributes such as coherence and curvature are also widely used. Most of these attributes correspond to a very simple easy-to understand measures of a waveform or surface morphology. However, not all geologic features can be so easily quantified. For this reason, simple statistical measures of the seismic waveform such as RMS amplitude prove to be quite valuable in delineating more chaotic stratigraphy. In this paper, we show how modern texture analysis based on the gray-level co-occurrence matrix, when coupled with recent developments in self-organizing maps clustering technology, extends such statistical measures to delineate features that geoscientists can see, but not easily describe.
One of the goals of the seismic interpreter is to analyze seismic amplitude and phase character in order to predict lithologic facies and rock properties such as porosity and thickness. Seismic attribute analysis is a technique that is commonly used by the oil industry to delineate stratigraphic and structural features of interest. Seismic attributes are particularly important in allowing the interpreter to enhance and visualize subtle features at or below the limits of seismic resolution. For example, coherence can generate easy-to-understand images of polygonally faulted shales that may be difficult to see on seismic amplitude time slices. Curvature can enhance long wavelength (500 -1000 m) flexures and folds in and out of the plane. Spectral components may highlight thin bed tuning effects buried in the seismic waveform.
Each of these attributes is based on a very simple geometric or physical model that can be related to structure, stratigraphy, diagenesis, or data quality. However, not all geologic features follow such a simple model. Experienced interpreters can easily recognize the seismic response of crystalline basement, mass transport complexes, and carbonate reef buildups. But when put to the task they find it difficult to quantitatively define how they do their interpretation. Such interpreters (and human beings in general) are experts at texture analysis. Our study focuses upon seismic texture analysis, borrowing upon techniques commonly used in remote sensing to map terrain, vegetation, and land-use information. Textures are frequently characterized as different patterns in the underlying data. Seismic texture analysis was first introduced by Love and Simaan (1984) to extract patterns of common seismic signal character. Recently, several workers (West et al., 2002; Gao, 2003; Chopra and Alexseev, 2005) have extended this technique to 3D seismic data through the use of the gray-level cooccurrence matrix (GLCM). GLCM allows the recognition of patterns significantly more complex than simple edges. GLCM-based texture attributes are able to delineate complicated geological features such as mass transport complexes and amalgamated channels that exhibit a distinct spatial pattern.
Texture is an everyday term relating to touch that includes such concepts as rough, silky, and bumpy. When a texture is rough to the touch, the surface exhibits sharp differences in elevation within the space of your fingertip.
