A fuzzy C-means clustering approach for petrophysical characterization of lithounits in the North Singhbhum Mobile Belt, Eastern India

The characterization of the various rock types through petrophysical data analysis is essential for comprehending geological processes and enhancing the efficacy of geophysical approaches aimed at mineralization zones. In the present study, a Fuzzy C-Means (FCM) clustering algorithm was employed to automatically classify lithounits within the western sector of the North Singhbhum Mobile Belt based on the petrophysical properties. Laboratory measurements of 326 rock samples from the study area show a wide range of density (~2350–3150 kg/m³) and magnetic susceptibility (10⁻⁵ SI to 10⁻¹ SI) values. Further FCM analysis reveals three distinct clusters: (i) cluster 1 displays high density and low magnetic susceptibility responses and comprises majorly metabasic, phyllite, and mica schist rocks, (ii) cluster 2 shows low density and low magnetic susceptibility characteristics and contains mainly metasedimentary rocks (phyllite, quartzite, and mica schist) and (iii) cluster 3 also primarily encompasses metasedimentary rocks, but it displays the low density and high magnetic susceptibility characteristics. Overlap of rock types in different clusters probably indicates the influence of secondary geological processes on the petrophysical measurements such as metamorphism, alteration, and weathering, which is also supported by the petrographical studies. Overall, the present study demonstrates the potential utility of the FCM algorithm for automatic lithology classification and inferring the associated geological processes from the petrophysical measurements. Furthermore, the correlation between the geophysical and petrophysical clusters highlights the role of petrophysical information in the automatic geological/mineral mapping. However, the complexity in cluster attributes on a detailed scale suggests that future studies in the NSMB should focus on comprehensive multi-parameter petrophysical and geochemical measurements. This approach will help in developing better strategies for 3D geophysical data inversion and resolve the complexities in petrophysical data interpretation.