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An Introduction to Paradise 3.2

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An Introduction to Paradise 3.2 ...
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Making Sense of Machine Learning

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Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning ...
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Comparison of Seismic Amplitude to SOM Classification

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Compare traditional seismic interpretation results with SOM (Self-Organizing Maps) classification achieved with machine learning in Paradise software ...
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Title slide for Marfurt presentation

Attribute Selection: Machine Learning vs. Interactive Interpretation

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Dr. Kurt Marfurt, Principal Investigator at the AASPI Consortium at the University of Oklahoma, shares insights on "Attribute Selection: Machine ...
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Paradise 3.2 Brochure Seismic Interpretation-min

Paradise 3.2

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Paradise 3.2 announcement ...
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Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

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Deborah Sacrey, Owner and Geophysicist of Auburn Energy, provides a review of the various attribute categories and their possible machine ...
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Solving Exploration Problems with Machine Learning Figure 7

Solving Exploration Problems with Machine Learning

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Geoscientists Deborah Sacrey and Rocky Roden solve exploration problems using Paradise, machine learning software for seismic interpretation in the June ...
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Machine Learning Terms

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A glossary defining essential machine learning terms within the seismic interpretation and geoscience community from Principal Component Analysis (PCA) to ...
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Thin Beds and Anomaly Resolution in the Niobrara

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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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