Title:  Texture and Human Vision System based Computational Seismic Interpretation

Committee: 

Dr. AlRegib, Advisor    

Dr. McClellan, Chair

Dr. Anderson

Abstract:

The objective of this research is to automate the process of structural interpretation of migrated seismic data. Establishing a close correspondence between seismic interpretation and area geology is a key prerequisite of any successful exploration and production project. To extract useful information from seismic volumes, interpreters manually delineate important geological structures such as salt domes, faults, and anticlines that contain hints about petroleum and gas reservoirs. These structures, which typically span over several square kilometers are delineated based on the correlation, changes in illumination, intensity, contrast, and the texture of seismic data. Manual delineation of such features is extremely time consuming and labor intensive and unfortunately, very few tools are available for automatic seismic interpretation. In this dissertation research, we propose novel seismic attributes based on texture dissimilarity, visual attention theory, and the modeling of the human visual system to quantify changes and highlight seismic features in three-dimensional space. To automate the process of seismic interpretation, we develop interpreter-assisted, fully-, and semi-automated workflows that are interactive and easy-to-use for the delineation of important geological structures within seismic volumes. Experimental results on real and synthetic datasets show that our proposed algorithms outperform state-of-the-art methods for seismic interpretation. The proposed research is computationally inexpensive and not only demonstrate a great potential for detecting and delineating important geological structures but also expected to reduce the time for seismic interpretation from years to months.