Self-Organizing Neural Nets for Automatic Anomaly Identification

Self-Organizing Neural Nets for Automatic Anomaly Identification

Self-organizing maps are a type of unsupervised neural network which fit themselves to the pattern of information in multi-dimensional data in an orderly fashion. The curvature and harvesting of the classification with low probability in a SOM are an indicator of multi-attribute anomalies for further investigation.

Distillation of Seismic Attributes to Geologic Significance

Distillation of Seismic Attributes to Geologic Significance

Seismic attributes identify many geologic features in seismic data where PCA helps identify optimal attributes and help determine which attributes to use in a multi-attribute analysis using SOM. The process in Paradise reveals natural clustering by pattern recognition in the data helping define aspects like stratigraphy, seismic facies, DHI features and sweet spots for shale.

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

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

Unsupervised neural network searches multi-dimensional data for natural clusters. Neurons are attracted to areas of higher information density. The SOM analysis relates to subsurface geometry and rock properties while noting multi-attribute seismic properties at the wells, correlating to rock lithologies, with those away from the wells.

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