Summary

In this paper we illustrate unsupervised and supervised learning algorithms that accurately classify the lithological variations in the 3D seismic data. We demonstrate blind source separation techniques such as the principal components (PCA) and noise adjusted principal components in conjunction with Kohonen Self organizing maps to produce superior unsupervised classification maps. Further, we utilize the PCA space training in Maximum likelihood (ML) supervised classification. Results demonstrate that the ML supervised classification produces an improved classification of the facies in the 3D seismic dataset from the Anadarko basin in central Oklahoma.

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