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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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Resolution of Faults in the Eagle Ford, Part 3

Resolution of Faults in the Eagle Ford, Part 3

Geophysical Insights
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Applying Principal Component Analysis (PCA) and Self-Organizing Map (SOM) process to show faults on the base amplitude seismic survey and ...
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Geologic Pattern recognition

Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps

Interpretation Journal
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Current computing technology has allowed for the application of new machine learning techniques in analyzing seismic data through pattern recognition ...
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Water Oil Contact - Conventional Multi-Attribute Analysis

Approach Aids Multiattribute Analysis

American Oil and Gas Reporter (AOGR)
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How Self-Orgazining Maps (SOM) and Principal Componenrt Analysis (PCA) greatly enhances the interpretation process to identify geology in diffferent settings ...
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2D Colormaps

Distillation of Seismic Attributes to Geologic Significance

Offshore Technology Conference
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Seismic attributes identify many geologic features in seismic data where PCA helps identify optimal attributes and help determine which attributes ...
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seismic interpretation software - SOM

Seismic Pattern Recognition in Shale Resource Plays

E&P Magazine
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Utilizing machine learning via Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) interpretation techniques to help identify sweet spots ...
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