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.
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.