  Seismic facies identification plays a significant role in reservoir
characterization. It helps in identifying the various lithological and
stratigraphical changes in reservoir properties. With the increase in the size
of seismic data or number of attributes to be analyzed, the manual process for
facies identification becomes complicated and time-consuming. Even though
seismic attributes add multiple dimensions to the data, their role in reservoir
characterization is very crucial. There exist different linear transformation
methods that use seismic attributes for identification, characterization, and
visualization of seismic facies. These linear transformation methods have been
widely used for facies characterization. However, there are some limitations
associated with these methods such as deciding the width parameters, number of
clusters, convergence rate etc. Therefore, the present research work uses
non-linear transformation approach that overcomes some of the major limitations
of linear approaches. The proposed Seismic facies analysis approach based on
Generative Topographic Map \& Radial Basis Function(SFA-GTM) works by
calculating the set of four Gray Level Co-occurrence Matrix(GLCM) texture based
attributes viz. energy, homogeneity, contrast, and dissimilarity. The
Generative Topographic Map(GTM) is used for unsupervised classification of
seismic facies based on the set of calculated texture attributes. Further, the
present work uses Radial Basis Function(RBF) for interpolating the missing
values in the data.
