  Support vector machines (SVMs) have been recognized as a potential tool for
supervised classification analyses in different domains of research. In
essence, SVM is a binary classifier. Therefore, in case of a multiclass
problem, the problem is divided into a series of binary problems which are
solved by binary classifiers, and finally the classification results are
combined following either the one-against-one or one-against-all strategies. In
this paper, an attempt has been made to classify lithology using a multiclass
SVM based framework using well logs as predictor variables. Here, the lithology
is classified into four classes such as sand, shaly sand, sandy shale and shale
based on the relative values of sand and shale fractions as suggested by an
expert geologist. The available dataset consisting well logs (gamma ray,
neutron porosity, density, and P-sonic) and class information from four closely
spaced wells from an onshore hydrocarbon field is divided into training and
testing sets. We have used one-against-all strategy to combine the results of
multiple binary classifiers. The reported results established the superiority
of multiclass SVM compared to other classifiers in terms of classification
accuracy. The selection of kernel function and associated parameters has also
been investigated here. It can be envisaged from the results achieved in this
study that the proposed framework based on multiclass SVM can further be used
to solve classification problems. In future research endeavor, seismic
attributes can be introduced in the framework to classify the lithology
throughout a study area from seismic inputs.
