Detection of iron-bearing mineral assemblages in Nainarmalai granulite region, south India, based on satellite image processing and geochemical anomalies.

Abstract

Although India's large iron ore reserves are well known, there are still few studies and research on iron ore prospecting in smaller deposits or the deposits with lower grade. Remote sensing concepts are useful to target for mineral exploration, since it covers a large area at low cost. In this research spectral remote sensing and digital image processing of ASTER data to locate and delineate the regions with iron oxide-bearing soils in granulite terrain at Nainnarmalai (southern India) that has hypothetical reserve 8.2 million tons of iron in sub-surface banded magnetite quartzites. The image-based study component involved pre-processing atmospheric correction and processing of the image data reminiscent of band ratio and band mixture to locate iron-bearing soils. We used blending of bands 5/3 + 1/2 to delineate ferrous iron oxide, band combination of 2/1 to delineate ferric iron oxide, and band ratio of 5/4 to delineate the lateritic soil. Further, the linear spectral unmixing outcome of ASRER data was evaluated concerning the ground truth of geochemical compositions of samples from the study area. Our results showed that image processing of the ASTER satellite data has the potential to delineate ferric, ferrous, and lateritic mineral assemblages in the iron-bearing soils with minimal requirement of ground truth verification. This research work aided in increasing trust in the use of space-based data for mineral prospecting. Image processing has demonstrated that ASTER data can be used to enhance iron ore exploration and the discovery of new mineralized areas.