In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Vi... more In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The primaryhost rocks of the Cu, Pb, Zn mineralization in the area are Banded Magnetite Quartzite (BMQ), unclassified calcareous silicates, and quartzite. We used ratio images derived from the scale and root mean squares (RMS) error imagesusing the multi-range spectral feature fitting (MRSFF) methodto delineate host rocks from the AVIRIS-NG image. The False Color Composites (FCCs) of different relative band depth images, derived from AVIRIS-NG spectral bands, were also used for delineating few minerals. These minerals areeither associated with the surface alteration resulting from the ore-bearing fluid migration orassociated with the redox-controlled supergene enrichments...
Future Challenges in Earth Sciences for Energy and Mineral Resources, 2016
Hyperspectral remote sensing is an advanced and up-coming technology in the field of remote sensi... more Hyperspectral remote sensing is an advanced and up-coming technology in the field of remote sensing. The Geological Survey of India (GSI) has taken a lead in building up a comprehensive spectral library for rocks and minerals for the entire Indian sub-continent in line with the spectral library of the United States Geological Survey (USGS) and the Jet Propulsion Laboratory (JPL). The readymade spectra of minerals (including ore minerals) and rocks (altered and unaltered) could help in the identification of the mineralogical composition of an unknown area and ultimately in targeting the mineralised zones. The study is aimed at the spectral signature of kaolinite in both the solar illumination in the field and in artificial illumination (tungsten quartz halogen lamp) as source in the laboratory condition. The diagnostic absorption features of kaolinite in visible and NIR wavelrngh (0.35-2.50 μm) are unavailable in the conventional remotely sensed data specially at 1.40 μm and 1.90 μm because of atmospheric obscuration. Kaolinite shows intense absorption at 1.396, 1.413, 1.913, 2.166 and 2.209 μm and comparatively less absorption features at 0.37, 0.97, 2.327, 2.356, 2.385 μm. The slightly broad absorption feature at 1.396 μm with a sharper band at 1.413 μm is because of the OH stretch overtones and features at 2.166 and 2.209 μm are because of Al- OH bend plus OH stretch combination. Doublet at 1.40 and 2.20μm is characteristic of kaolinite.
Abstract Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GS... more Abstract Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.
In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Vi... more In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The primaryhost rocks of the Cu, Pb, Zn mineralization in the area are Banded Magnetite Quartzite (BMQ), unclassified calcareous silicates, and quartzite. We used ratio images derived from the scale and root mean squares (RMS) error imagesusing the multi-range spectral feature fitting (MRSFF) methodto delineate host rocks from the AVIRIS-NG image. The False Color Composites (FCCs) of different relative band depth images, derived from AVIRIS-NG spectral bands, were also used for delineating few minerals. These minerals areeither associated with the surface alteration resulting from the ore-bearing fluid migration orassociated with the redox-controlled supergene enrichments...
Future Challenges in Earth Sciences for Energy and Mineral Resources, 2016
Hyperspectral remote sensing is an advanced and up-coming technology in the field of remote sensi... more Hyperspectral remote sensing is an advanced and up-coming technology in the field of remote sensing. The Geological Survey of India (GSI) has taken a lead in building up a comprehensive spectral library for rocks and minerals for the entire Indian sub-continent in line with the spectral library of the United States Geological Survey (USGS) and the Jet Propulsion Laboratory (JPL). The readymade spectra of minerals (including ore minerals) and rocks (altered and unaltered) could help in the identification of the mineralogical composition of an unknown area and ultimately in targeting the mineralised zones. The study is aimed at the spectral signature of kaolinite in both the solar illumination in the field and in artificial illumination (tungsten quartz halogen lamp) as source in the laboratory condition. The diagnostic absorption features of kaolinite in visible and NIR wavelrngh (0.35-2.50 μm) are unavailable in the conventional remotely sensed data specially at 1.40 μm and 1.90 μm because of atmospheric obscuration. Kaolinite shows intense absorption at 1.396, 1.413, 1.913, 2.166 and 2.209 μm and comparatively less absorption features at 0.37, 0.97, 2.327, 2.356, 2.385 μm. The slightly broad absorption feature at 1.396 μm with a sharper band at 1.413 μm is because of the OH stretch overtones and features at 2.166 and 2.209 μm are because of Al- OH bend plus OH stretch combination. Doublet at 1.40 and 2.20μm is characteristic of kaolinite.
Abstract Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GS... more Abstract Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.
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