Support vector machine-based integration of AVIRIS NG hyperspectral and ground geophysical data for identifying potential zones for chromite exploration – A study in Tamil Nadu, India

Abstract

In the present study, we have integrated the spectral anomaly map derived from airborne visible-infrared imaging spectrometer-next generation (AVIRIS-NG) data and residual ground geophysical anomaly maps (gravity and magnetic) for identifying the mafic cumulates and chromitites in the Sittampundi Layered-Complex (SLC), Tamil Nadu, India. A spectral map of chromite bearing mafic cumulates was prepared by implementing the matched filtering (MF) algorithm on the selected spectral bands of AVIRIS-NG data using the mixed image spectra of mafic cumulate and chromitite as an end member. A low pass filter was applied in the spectral map to extract the clusters of coherently distributed spectrally anomalous pixels representing the surface exposure of mafic cumulates. Concurrently, the ground gravity and magnetic data were processed for deriving the residual gravity and magnetic anomaly maps. Subsequently, an integration of spectral map, residual gravity, and magnetic anomaly maps was attempted using the support vector machine (SVM) algorithm to identify the potential sites of chromitite occurrence. Results were verified using the geological map and the field data on surface exposures of mafic cumulates. This research demonstrates that integration of remote sensing data and geophysical anomaly maps has immense potential to detect the surface distribution of chromite bearing mafic cumulates in the layered complex and the same methods can be replicated for carrying out mineral prospecting in the other parts of the world.