Processing and Analysis of Hyperspectral Fingerprints to Characterise Haematite of Singbhum Iron Ore Belt, Orissa, India
Keywords: Hyperspectral fingerprints, geochemical analysis, statistical analysis, iron ores grades, Singhbhum iron ore belt
Abstract. The demand for iron ore has been increasing in the recent years, thereby requiring the adoption of fast and accurate approaches to iron ore exploration and its grade-assessment. It is in this context that hyperspectral sensing is deemed as a potential tool. This paper examines the potential of hyperspectral fingerprints in the visible, NIR and SWIR regions of the EMR to assess the grades of haematite of the western Singhbhum iron ore belt of Orissa, eastern India, in a rapid manner. Certain spectro-radiometric measurements and geochemical analysis were carried out and the results have been presented. From the spectral measurements, it is seen that the strength of reflectance and absorption at definite wavelength regions is controlled by the chemical composit ion of the iron ores. It is observed that the primary spectral characteristics of these haematites lie in the 650–750 nm, 850 to 900 nm and 2130–2230 nm regions. The laboratory based hyperspectral fingerprints and multiple regression analysis of spectral parameters and geochemical parameters (Fe% and Al2O3%) predicted the concentration of iron and alumina content in the haematite. A very strong correlation (R2 = 0.96) between the spectral parameters and Fe% in the haematite with a minimum error of 0.1%, maximum error of 7.4% and average error of 2.6% is observed. Similarly, a very strong correlation (R2 = 0.94) between the spectral parameters and Al2O3% in the iron ores with a minimum error of 0.04%, maximum error of 7.49% and average error of 2.5% is observed. This error is perhaps due to the presence of other components (SiO2, TiO2, P2O etc.) in the samples which can alter the degree of reflectance and hence the spectral parameters. Neural network based multi-layer perception (MLP) analysis of various spectral parameters and geochemical parameters helped to understand the relative importance of the spectral parameters for predictive models. The strong correlations (Iron: R2 = 0.96; Alumina: R2 = 0.94) indicate that the laboratory hyperspectral signatures in the visible, NIR and SWIR regions can give a better estimate of the grades of haematite in a rapid manner.