Texture model regression for effective feature discrimination: Application to seismic facies visualization and interpretation

Abstract

The classical approach to feature discrimination requires extraction and classification of multiple attributes. Such an approach is expensive in terms of computational time and storage space, and the results are generally difficult to interpret. With increasing data size and dimensionality, along with demand for high performance and productivity, the effectiveness of a feature‐discrimination methodology has become a critically important issue in many areas of science. To address such an issue, I developed a texture model regression (TMR) methodology. Unlike classical attribute extraction and classification algorithms, the TMR methodology uses an interpreter‐defined texture model as a calibrating filter and regresses the model texture with the data texture at each sample location to create a regression‐gradient volume. The new approach not only dramatically reduces computational cycle time and space but also creates betters results than those obtained from classical techniques, resulting in improved feature discrimination, visualization, and interpretation.Application of the TMR concept to reflection seismic data demonstrates its value in seismic‐facies analysis. In order to characterize reflection seismic images composed of wiggle traces with variable amplitude, frequency, and phase, I introduced two simple seismic‐texture models in this application. The first model is defined by a full cycle of a cosine function whose amplitude and frequency are the maximum amplitude and dominant frequency of wiggle traces in the interval of interest. The second model is defined by a specific reflection pattern known to be associated with a geologic feature of interest, such as gas sand in a hydrocarbon reservoir. I applied both models to a submarine turbidite system offshore West Africa and to a gas field in the deep‐water Gulf of Mexico, respectively. Based on extensive experimentation and comparative analysis, I found that the TMR process with such simple texture models creates superior results, using minimal computational resources. The result is geologically intriguing, easily interpretable, and consistent with general depositional and reservoir‐facies concepts. Such a successful application may be attributable to the sensitivity of image texture to physical texture in the Fresnel zone at an acoustic interface and therefore to lithology, depositional facies, and hydrocarbonsaturation.