Geophysical Insights hosting the 2018 OIl & Gas Machine Learning Symposium in Houston on September 27, 2018
Dr. Tom Smith presenting on Machine Learning at the 3D Seismic Symposium on March 6th in Denver
What is the "holy grail" of Machine Learning in seismic interpretation? by Dr. Tom Smith, GSH Luncheon 2018
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
 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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