A Visual Data Mining Methodology to Conduct Seismic Facies Analysis: Part 2 - Application to 3D Seismic Data
A visual data-mining approach to unsupervised clustering analysis can be an effective tool for visualizing and understanding patterns inherent in seismic data i.e., seismic facies. The unsupervised clustering analysis is completely data-driven, requiring no external information e.g., well logs to guide the seismic-trace classification. The paper demonstrates the application of the visual data-mining approach to seismic facies analysis on a real 3D seismic data volume. The paper selects two stratigraphic intervals, the first including a Devonian pinnacle reef system and the second containing a Jurassic siliciclastic channel system. Both analyses show major stratigraphic features that can be defined in horizon slices or other types of visualization. However, the visual data-mining approach creates seismic facies maps with improved visual detail, distinguishing seismic trace-shape variability in the data.