The use of automatic seismic facies classification techniques has been steadily increasing within E&P interpretation workflows over the past 10 years. It is not yet considered a standard procedure but, with the knowledge of the advantages (and limitations) of the different seismic classification methods, its role in the interpretation process as a successful hydrocarbon prediction tool is anticipated to grow.

This paper reviews and compares the unsupervised classification methods presently used in seismic facies analysis: K-means clustering, principal component analysis (PCA), projection pursuit, and neural networks (vector quantization and Kohonen self-organizing maps). The term “unsupervised” covers all classification techniques relying...

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