Earlier this week, Rocky Roden's presentation on "Interpreting Below Seismic Tuning Using Multi-Attribute Analysis" garnered quite the buzz. Geosicentists across the globe learned of new processes in visualizing thin beds and facies with machine learning technology. In the presentation, Mr. Roden covered topics such as:
Rayleigh's Criterion and the classical basis of seismic tuning
Work by Brown et al. (1984,1986) and Connolly (2007) on thin bed calculations
Phenomena at or below tuning
Applications of attributes to the wedge model
How multi-attribute classification techniques that use machine learning enable visualization below tuning
Case studies in the application of this new technique in conventional and unconventional geologic settings
Rocky R. Roden has extensive knowledge of modern geoscience technical approaches (past Chairman-The Leading Edge Editorial Board). As former Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia. He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East. Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco. He holds a B.S. in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M University.