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Applications of Convolutional Neural Networks (CNN) to Seismic Interpretation

Applications of Convolutional Neural Networks (CNN) to Seismic Interpretation

As part of our quarterly series on machine learning, we were delighted to have had Dr. Tao Zhao present applications of Convolutional Neural Networks (CNN) in a worldwide webinar on ...
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Future of Seismic Interpretation with Machine Learning and Deep Learning

Future of Seismic Interpretation with Machine Learning and Deep Learning

By: Iván Marroquín, Ph.D. - Senior Research Geophysicist I am very excited to participate as a speaker in the workshop on Big Data and Machine Learning organized by European Association ...
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Neural Network Classification

Unsupervised vs. Supervised classifiers – Comparing classification results

By: Ivan Marroquin, Ph.D. - Senior Research Geophysicist In machine learning, there is a very interesting challenge in comparing the quality of the classification result generated by either unsupervised or ...
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Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Self-Organizing Maps (SOM) is a relatively new approach for seismic interpretation in our industry and should not be confused with seismic inversion or rock modeling.  The descriptions below differentiate SOM, ...
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Austin chalk SOM image

The Value of Instantaneous Attributes

Self-Organizing Maps (SOM) is a relatively new approach for seismic interpretation in our industry and should not be confused with seismic inversion or rock modeling.  The descriptions below differentiate SOM, ...
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Thumbnail of colorful neurons

Machine Learning – The Next Generation Seismic Interpretation

Most people associate neural networks, big data and big number crunching as parts of a single paradigm for access to web information. Articulate a query and wait for a list of ...
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Image of truck

Machine Learning and Truck Driving

Every day, 2.5 quintillion bytes of data get created and in the last two years, 90% of the world's data was generated. So much information is available to us and ...
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