Watch videos of Presentations from SEG 2017
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning
Seismic interpretation and machine learning by Rocky Roden and Deborah Sacrey, GeoExPro, December 2016


Geophysical Insights at Society of Exploration Geophysicists Annual Conference and Exhibition

Multi-Attribute Analysis enables Interpretation Below Seismic Resolution


Machine learning, a method of data analysis that automates analytical model building, has moved beyond the realm of self-driving cars, banking fraud detection, and large-scale consumer behavior analysis and has begun to show major successes when applied to interpreting seismic data.  

Using algorithms that iteratively learn from data, machine learning allows interpreters to analyze and visualize multiple seismic attributes simultaneously, thereby revealing features below tuning - difficult to visualize by any other method. 

Paradise, from Geophysical Insights, is the industry's first and most robust machine learning engine applied specifically to seismic interpretation.  Using self-organizing maps within Paradise, interpreters can identify geologic features and stratigraphy well below typical seismic resolution or tuning thickness (1/4 wavelength thickness).  In this white paper, "Case Studies in Sub-seismic Resolution", several interpretation case studies outline how this technology was used to gain greater insights.  


To get your copy of "Case Studies in Sub-seismic Resolution" and learn how interpreters are using machine learning to extract more information from seismic data, please provide the following in formation.

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