About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
SEG 2019
Conference paper
Analysis of seismic and texture attributes for stratigraphic segmentation
Abstract
For decades, geoscientists have employed seismic attributes to improve their understanding of the subsurface. More recently, image-based texture attributes started to be considered in the geoscience community to describe the texture patterns found in seismic data. In this context, many works have investigated seismic and texture attributes and their combination for different tasks in seismic interpretation. This work aims to analyze the performance of seismic and texture attributes for a machine learning task from a feature selection perspective. We select the most discriminative attributes for a classification and a clustering task related to the stratigraphic segmentation of a public seismic dataset. The results indicate that texture attributes may be more suitable for these tasks than seismic attributes. Ten out of ~60 attributes selected with the ANOVA feature selection algorithm achieved 0.85 of F1 Score and 0.67 of V-Measure for the classification and clustering tasks, respectively.