Anomaly detection using dynamic Neural Networks, classification of prestack data

SUMMARY Automatic seismic facies classification is now common practice in the oil and gas industry. Unfortunately unsupervised seismic classification is often not optimal. The main criticism of unsupervised classification is the a priori nature of the seismic data set organization and the poor description of seismic due to data redundancy. Data reduction, such as Principal Component Analysis (PCA) is often used in association to reveal the principal characteristics of the geological system. The new clustering described here will with a dynamic process naturally search to fill the data space, and to describe the full variability of the seismic. The process can be imagined as a gas expanding in volume. Finally, the process details the anomalies which potentially correspond to hydrocarbon