Resource Library

Webinar Intro Tao Zhao 20Mar2019

Video: Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

Geophysical Insights
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Dr. Tao Zhao discusses how deep learning technology, namely convolutional neural networks (CNN), is revolutionizing seismic data interpretation like seismic ...
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Image of Seismic amplitude co-rendered Lines A and B

A Fault Detection Workflow Using Deep Learning and Image Processing

SEG Annual Meeting
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Tao Zhao unveils a fault detection workflow using deep learning and image processing technologies at the 2018 Annual SEG Meeting ...
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Holy Grail Slideshow Title Card

The Holy Grail of Machine Learning in Seismic Interpretation

Geophysical Society of Houston (GSH)
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Dr. Tom Smith shares the "Holy Grail" of Machine Learning in Seismic Interpretation with the Geophysical Society of Houston ...
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Title slide for geobodies in paradise

Geobodies in Paradise: a Machine Learning Application

Geophysical Insights
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Dr. Tom Smith presents "Geobodies in Paradise: a Machine Learning Application" at the 2018 SEG Convention in Anaheim, California. Dr ...
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CNN_facies

Seismic Facies Classification Using Deep Convolutional Neural Networks

SEG Annual Meeting
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Using a new supervised learning technique, convolutional neural networks (CNN), interpreters are approaching seismic facies classification in a revolutionary way ...
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Seismic cube layers

An Introduction to Paradise 3.2

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

Geophysical Insights
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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

Geophysical Insights
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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

Geophysical Insights
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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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