Deep Embedded Clustering as a Seismic Attribute: A Case Study of 2D Crustal-Scale Interpretation

Summary Deep embedded K-means clustering algorithm is applied to several 2D crustal-scale seismic profiles to highlight the distribution of reflections and investigate the complexity of geological structures better across the profiles. Such clustering proves to be a great interpretation asset for long, regional profiles, helping to delineate various crustal units.