Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data
Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data
عنوان فارسی :
Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data
عنوان انگلیسی :
Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data
چکیده انگلیسی:
The purpose of mineral exploration is to find ore deposits. The main aim of this work is to use the fuzzy inference system to integrate the exploration layers including the geological, remote sensing, geochemical, and magnetic data. The studied area was the porphyry copper deposit of the Kahang area in the preliminary stage of exploration. Overlaying of rock units and tectonic layers were used to prepare the geological layer. ASTER images were used for the purpose of recognition of the alterations. The processes used for preparation of the alteration layer were the image-based methods including RGB, band ratio, and principal component analysis as well as the spectrum-based methods including spectral angel mapper and spectral feature fitting. In order to prepare the geochemical layer, the multivariate statistical methods such as the Pearson correlation matrix and cluster analysis were applied on the data, which showed that both copper and molybdenum were the most effective elements of mineralization. Application of the concentration-number multi-fractal modeling was used for geochemical anomaly separation, and finally, the geochemical layer was obtained by the overlaying of two prepared layers of copper and molybdenum. In order to prepare the magnetics layer, the analytical signal map of the magnetometry data was selected. Finally, the FIS integration was applied on the layers. Ultimately, the mineral potential map was obtained and compared with the 33 drilled boreholes in the studied area. The accuracy of the model was validated upon achieving the 70.6% agreement percentage between the model results and true data from the boreholes, and consequently, the appropriate areas were suggested for the subsequent drilling.
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