Multi-Scale Potential Field Data Integration Using Fuzzy C-Means Clustering for Automated Geological Mapping of North Singhbhum Mobile Belt, Eastern Indian Craton

Abstract

Fuzzy C-Means (FCM) clustering is an unsupervised machine learning algorithm that helps to integrate multiple geophysical datasets and provides automated objective-oriented information. This study analyzed ground-based Bouguer gravity and aeromagnetic datasets using the FCM clustering algorithm to classify lithological units in the western part of the North Singhbhum Mobile Belt, a mineralized belt in the Eastern Indian Craton. The potential field signatures of clusters obtained using FCM correlate remarkably well with the existing surface geology on a broad scale. The cluster associated with the highest gravity signatures corresponds to the metabasic rocks, and the cluster with the highest magnetic response represents the mica schist rocks. The cluster characterized by the lowest gravity and magnetic responses reflects the granite gneiss rocks. However, few geological formations are represented by two or more clusters, probably due to the close association of similar rock types. The fuzzy membership scores for most of the data points in each cluster show above 0.8, indicating a consistent relationship between geophysical signatures and the existing lithological units. Further, the study reveals that integrating multi-scale geophysical data helps to disclose bedrock information and litho-units under the sediment cover.