Characterization of Gold Mineralization in Northern Pakistan Using Imaging Spectroscopy

Khan, Shuhab D.; Okyay, U.; Ahmad, Laeiq; Shah, Mohammad Tahir

doi:10.14358/pers.84.7.425

Cited by 10 publications

(4 citation statements)

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“…The reflectance curve contains physical and chemical properties of the material since chemical bonds absorb light at specific wavelengths [21]. Ground-based hyperspectral imaging has been widely used in geologic characterizations [15,[22][23][24][25][26][27][28], in which variations of the sub-centimeter or sub-millimeter scale can be resolved. This study used hyperspectral imaging to identify mineralogy as well as to extract relative abundances of the minerals.…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

Sun

Khan

Shabestari³

2019

Remote Sensing

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The Goldstrike district in southwest Utah is similar to Carlin-type gold deposits in Nevada that are characterized by sediment-hosted disseminated gold. Suitable structural and stratigraphic conditions facilitated precipitation of gold in arsenian pyrite grains from ascending gold-bearing fluids. This study used ground-based hyperspectral imaging to study a core drilled in the Goldstrike district covering the basal Claron Formation and Callville Limestone. Spectral modeling of absorptions at 2340, 2200, and 500 nm allowed the extraction of calcite, clay minerals, and ferric iron abundances and identification of lithology. This study integrated remote sensing and geochemistry data and identified an optimum stratigraphic combination of limestone above and siliciclastic rocks below in the basal Claron Formation, as well as decarbonatization, argillization, and pyrite oxidation in the Callville Limestone, that are related with gold mineralization. This study shows an example of utilizing ground-based hyperspectral imaging in geological characterization, which can be broadly applied in the determination of mining interests and classification of ore grades. The utilization of this new terrestrial remote sensing technique has great potentials in resource exploration and exploitation.

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Section: Hyperspectral Imagingmentioning

confidence: 99%

Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

Sun

Khan

Shabestari³

2019

Remote Sensing

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“…Remote sensing studies for geology, engineering, and agriculture are greatly improved by spectral information that makes it easier to identify materials, as well as biological and chemical properties through the spectroscopy [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Spectral information is usually given by the reflectance level for a specific wavelength that is emitted by a common source. The spectroscopy [ 1 ] study the reflected wavelengths of both visible and invisible light e.g., red, green, and blue colors, visible near-infrared (VNIR), short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) [ 4 ]. As different materials reflect the electromagnetic energy in different wavelengths, they can be identified by analyzing the reflectance, given a series of wavelengths that characterizes the spectral signature of the material [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Spectral images can be acquired through sub-orbital survey, which is the case of spectral sensors onboard of airplanes and unmanned aerial vehicles (UAV), and by orbital surveys, through orbiting satellites [ 1 , 2 , 3 , 4 ]. These techniques are part of the remote sensing studies that also include traditional imagery acquisition (photos), photogrammetry, Light Detection and Ranging (LiDAR) scannings, and radiometry.…”

Section: Introductionmentioning

confidence: 99%

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Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks

Marques

Souza

Müller

et al. 2020

Sensors

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Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural networks have been tested with encouraging results. However, they require large datasets, more time and computational power for training. To overcome these limitations, this work aims to increase the spatial resolution of multispectral bands from the Landsat satellite database using a modified artificial neural network that uses pixel kernels of a single spatial high-resolution RGB image from Google Earth as input. The methodology was validated with a common dataset of indoor images as well as a specific area of Landsat 8. Different downsized scale inputs were used for training where the validation used the ground truth of the original size images, obtaining comparable results to the recent works. With the method validated, we generated high spatial resolution spectral bands based on RGB images from Google Earth on a carbonated outcrop area, which were then properly classified according to the soil spectral responses making use of the advantage of a higher spatial resolution dataset.

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Gold mineralization in Bubin area, Gilgit-Baltistan, Northern Areas, Pakistan

et al. 2018

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Characterization of Gold Mineralization in Northern Pakistan Using Imaging Spectroscopy

Cited by 10 publications

References 0 publications

Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks

Gold mineralization in Bubin area, Gilgit-Baltistan, Northern Areas, Pakistan

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