Resource Library
Machine Learning Terms
Geophysical Insights
A glossary defining essential machine learning terms within the seismic interpretation and geoscience community from Principal Component Analysis (PCA) to ...
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Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps
Interpretation Journal
Current computing technology has allowed for the application of new machine learning techniques in analyzing seismic data through pattern recognition ...
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Geobodies in Paradise: a Machine Learning Application
Geophysical Insights
Dr. Tom Smith presents "Geobodies in Paradise: a Machine Learning Application" at the 2018 SEG Convention in Anaheim, California. Dr ...
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Making Sense of Machine Learning
Geophysical Insights
Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning ...
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Attribute Selection: Machine Learning vs. Interactive Interpretation
Geophysical Insights
Dr. Kurt Marfurt, Principal Investigator at the AASPI Consortium at the University of Oklahoma, shares insights on "Attribute Selection: Machine ...
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Comparison of Seismic Amplitude to SOM Classification
Geophysical Insights
Compare traditional seismic interpretation results with SOM (Self-Organizing Maps) classification achieved with machine learning in Paradise software ...
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Paradise 3.2
Geophysical Insights
Geophysical Insights announces the launch of Paradise 3.2, which includes the isolation of geobodies through machine learning, and the generation ...
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Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data
Geophysical Insights
Deborah Sacrey, Owner and Geophysicist of Auburn Energy, provides a review of the various attribute categories and their possible machine ...
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Thin Beds and Anomaly Resolution in the Niobrara
American Oil and Gas Reporter (AOGR)
Using machine learning to classify a 100-square-mile seismic volume in the Niobrara, geoscientists were able to interpret thin beds below ...
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Attribute Essentials: Categories of Attributes
Geophysical Insights
A review of the various attribute categories and their possible application ...
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning
SPE Norway
The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir ...
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Seismic Interpretation of DHI Characteristics with Machine Learning
Geophysical Insights
The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this ...
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Machine Learning Revolutionizing Seismic Interpretation
American Oil and Gas Reporter (AOGR)
The science of petroleum geophysics is changing, driven by the nature of the technical and business demands facing geoscientists as ...
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Interpretation of DHI Characteristics with Machine Learning
First Break
Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale ...
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Seismic Interpretation Below Tuning with Multiattribute Analysis
The Leading Edge
Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale ...
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