A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
This work addresses seismic interpretation for geologists by applying existing texture attributes to a new domain, making it incremental.
The paper tackled the problem of characterizing subsurface geological structures in seismic volumes by conducting a comparative study of texture attributes from image processing, proving the feasibility of using these attributes for automatic seismic volume labeling to initiate interpretation more effectively.
In this paper, we explore how to computationally characterize subsurface geological structures presented in seismic volumes using texture attributes. For this purpose, we conduct a comparative study of typical texture attributes presented in the image processing literature. We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i.e., seismic volume labeling. For this application, a data volume is automatically segmented into various structures, each assigned with its corresponding label. If the labels are assigned with reasonable accuracy, such volume labeling will help initiate an interpretation process in a more effective manner. Our investigation proves the feasibility of accomplishing this task using texture attributes. Through the study, we also identify advantages and disadvantages associated with each attribute.