## 1 Introduction  
Lithology refers to the composition or type of rock in the Earth's subsurface. The term lithology is used as a gross description of a rock layer in the subsurface and uses familiar names, including sandstone, claystone (clay), shale (mudrock), siltstone, and so forth. In earth science, subsurface properties such as lithology identification have always been among the basic problems. A number of methods in lithology interpretation have been proposed by researchers. Conventional methods that employ seismic data for the estimation of reservoir lithology properties consist of finding a physical relationship between the properties to be identified and the seismic attributes and then employ that attribute over the entire seismic dataset in order to predict the target properties. Many seismic datasets are inundated with noise values, e.g., sensor noisy responses or equipment mismeasurements. In some cases, when the functional relationships between the attributes and the target properties can be found, the physical foundation is not often clear or understandable. In contrast, inferring lithology properties from the recorded well logs is much more reliable but economically expensive, challenging and time consuming. The lithology of a layer can also be identified by drilling holes, although this method often does not provide exact information. We can also obtain results from recorded continuous cores that are very expensive and might be unprofitable. The lithology can also be interpreted by geophysical inversion and geophysical modelling methods. The interpretation of lithology using electrofacies from well logs multi-attribute data has become one of the most prominent techniques used by several sectors of petroleum engineering, including geological studies for reservoir characterization, although petrophysical well logs contain uncertainty and noise. Clustering the recorded well logs is done to find similar or dissimilar patterns among the well log values in the multivariate space with an aim to group them together into individual classes called electrofacies [1]. Electrofacies have a unique set of log responses that can separate the material properties of the rocks and fluids contained in the volume recorded using the well-logging tools. These identified electrofacies can interpret and reflect the lithologic, diagenetic and hydrologic characteristics of an uncored well. By applying the additional information such as geological insight or core observations, the identified electrofacies (EF) clusters can be calibrated to ensure their interpretable geological meaning, and for this, the electrofacies classification process needs to be explained efficiently. Ideally, for defining electrofacies, there is no exact method. The electrofacies can be defined on the basis of standard well-log data, such as neutron porosity, gamma ray, resistivity, bulk density, caliper log, photoelectric effect, and so forth. Furthermore, they can often be associated to one or more lithofacies. Conventionally, lithofacies have been identified manually, depending on the core description and their relation to well-logs. The usual prerequisites are that the electrofacies should be defined from a reliable log set of petrophysical measurements and the similarities or dissimilarities among the down-hole intervals needs to be explained quantitatively from the sample log responses. This research proposes:  
1. An extra trees classifier (ETC)-based well log feature selection method. 2. A critical \"whitebox\" approach using the rule induction algorithm of rough set theory (RST) towards classifying the electrofacies which have been constructed by the non-hierarchical K-means clustering algorithm. 3. A unique method to determine the lithology from the electrofacies by employing the RST rules. RST is a machine learning tool which performs granular computation in a vague idea (set) based on two vivid sets of concepts: lower approximations and upper approximations.