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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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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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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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Seismic interpretation workflow with Paradise

Advancing Seismic Research with Modular Frameworks

Oilfield Technology
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New technology for the Big Data problem of the seismic volume using Unsupervised Neural Networks for the interpretation of Greenfield ...
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Time slice

Seismic Attribute Analysis Can Benefit From Unsupervised Neural Network

Offshore Magazine
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Process identifies anomalies from original data without bias using Unsupervised Neural Networks in Greenfield Exploration ...
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Introduction to Self-Organizing Maps in Multi-Attribute Seismic Data

Introduction to Self-Organizing Maps in Multi-Attribute Seismic Data

Geophysical Society of Houston (GSH)
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Unsupervised neural network searches multi-dimensional data for natural clusters. Neurons are attracted to areas of higher information density. The SOM ...
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