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Seismic facies identification plays a significant role in reservoircharacterization. It helps in identifying the various lithological andstratigraphical changes in reservoir properties. With the increase in the sizeof seismic data or number of attributes to be analyzed, the manual process forfacies identification becomes complicated and time-consuming. Even thoughseismic attributes add multiple dimensions to the data, their role in reservoircharacterization is very crucial. There exist different linear transformationmethods that use seismic attributes for identification, characterization, andvisualization of seismic facies. These linear transformation methods have beenwidely used for facies characterization. However, there are some limitationsassociated with these methods such as deciding the width parameters, number ofclusters, convergence rate etc. Therefore, the present research work usesnon-linear transformation approach that overcomes some of the major limitationsof linear approaches. The proposed Seismic facies analysis approach based onGenerative Topographic Map \& Radial Basis Function(SFA-GTM) works bycalculating the set of four Gray Level Co-occurrence Matrix(GLCM) texture basedattributes viz. energy, homogeneity, contrast, and dissimilarity. TheGenerative Topographic Map(GTM) is used for unsupervised classification ofseismic facies based on the set of calculated texture attributes. Further, thepresent work uses Radial Basis Function(RBF) for interpolating the missingvalues in the data.

SFA-GTM: Seismic Facies Analysis Based on Generative Topographic Map and RBF