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Multi-Attribute Seismic Surveys by Tom Smith

Advantages of Machine Learning with Multi-Attribute Seismic Surveys

Geophysical Insights
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Dr. Tom Smith, the President and CEO of Geophysical Insights, presents to the University of Houston Wavelet Society on the ...
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Machine Learning Essentials Course

NEW e-Course by Dr. Tom Smith: Machine Learning Essentials for Seismic Interpretation

Geophysical Insights
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Machine learning is foundational to the digital transformation of the oil & gas industry and will have a dramatic impact ...
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Future of Seismic Interpretation with Machine Learning and Deep Learning

Future of Seismic Interpretation with Machine Learning and Deep Learning

Geophysical Insights
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By: Iván Marroquín, Ph.D. - Senior Research Geophysicist I am very excited to participate as a speaker in the workshop ...
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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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Banner image for Solving Interpretation Problems Using Machine Learning

Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Geophysical Insights
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Deborah Sacrey of Auburn Energy hosts a webinar addressing challenges like interpretation of thin bedded reservoirs far below conventional seismic ...
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Image of conventional stacked seismic amplitudes

Interpretation of DHI Characteristics with Machine Learning

First Break
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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 with Machine Learning

Seismic Interpretation with Machine Learning

GeoExpro
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Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with ...
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Thumbnail of future interpreter video

A Profile of the Future Interpreter: A Movie by Dr. Kurt Marfurt of the AASPI Consortium, O.U.

Geophysical Insights
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Dr. Kurt Marfurt of the AASPI Consortium at The University of Oklahoma presents an example of how interpreters are using ...
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BEG Line 53 Full Classification - Copy

Self-Organizing Neural Nets for Automatic Anomaly Identification

Geophysical Insights
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Self-organizing maps are a type of unsupervised neural network which fit themselves to the pattern of information in multi-dimensional data ...
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