Improved feature extraction in seismic data: multi-attribute study from principal component analysis

Seismic attribute analysis is the most effective method to predict geological features from seismic images. However, using a single attribute for the purpose may reduce the prediction quality. Therefore, integrating multiple attributes becomes significant and has the potential to decipher the finer details. The present study uses the principal component analysis to carry out a multi-attribute study for long offset (5 km) seismic data from the Krishna-Godavari basin, Eastern Margin of India, so as to improve the seismic image and aid in feature extraction. The colour composites developed for the seismic data indicate improved continuity in reflectors in stratigraphic attributes analysis and better resolution of the faults and migration pathways in structural attributes analysis.