  Water saturation is an important property in reservoir engineering domain.
Thus, satisfactory classification of water saturation from seismic attributes
is beneficial for reservoir characterization. However, diverse and non-linear
nature of subsurface attributes makes the classification task difficult. In
this context, this paper proposes a generalized Support Vector Data Description
(SVDD) based novel classification framework to classify water saturation into
two classes (Class high and Class low) from three seismic attributes seismic
impedance, amplitude envelop, and seismic sweetness. G-metric means and program
execution time are used to quantify the performance of the proposed framework
along with established supervised classifiers. The documented results imply
that the proposed framework is superior to existing classifiers. The present
study is envisioned to contribute in further reservoir modeling.
