Exploration
mobin saremi; Abbas Maghsoudi; Reza Ghezelbash; mahyar yousefi; Ardeshir Hezarkhani
Abstract
Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were ...
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Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were employed in alignment with the conceptual model of these deposits. These layers encompass fault density, proximity to intrusive rocks, multi-element geochemical anomalies, and distances to phyllic and argillic alterations. The geochemical anomaly maps, recognized as the most effective layers, were generated through staged factor analysis (SFA) and the geochemical mineralization probability index (GMPI). Other layers were weighted using a logistic function, and their values were transformed into 0 -1 interval. Ultimately, to integrate the weighted layers, the fuzzy gamma operator and the geometric average method were applied. The normalized density index and prediction-area (P-A) plot were employed to evaluate the MPM models. The findings indicate that the developed models possess considerable validity and can be effectively utilized for planning future exploration endeavors.
Exploration
Babak Sohrabian; Abdullah Erhan Tercan
Abstract
Mineral Resources have commonly been estimated through the kriging method that assigns weights to the samples based on variogram distance to the estimation point without considering their values. More robust estimators such as spatial copulas are promising tools because they consider both distance ...
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Mineral Resources have commonly been estimated through the kriging method that assigns weights to the samples based on variogram distance to the estimation point without considering their values. More robust estimators such as spatial copulas are promising tools because they consider both distance and sample values in determining weights. The purpose of this study is to demonstrate the effectiveness of the Gaussian copulas (GC) by estimating the copper grade values in the Sungun porphyry copper deposit located in Iran. Performance of the method was compared to ordinary kriging (OK) and indicator kriging (IK) by running the Jackknife test of cross-validation. The metrics used in measuring performance of the methods are global accuracy and precision of the distribution of the estimates, error statistics, and variability for globally accurate and precise estimates. The case study shows advantages of GC over OK and IK by producing globally accurate and precise estimates with acceptable error statistics and variability.
Exploration
M. Honarmand; H. Ranjbar; H. Shahriari; F. Naseri
Abstract
This research was performed with the objective of evaluating the accuracy of spectral angle mapper (SAM) classification using different reference spectra. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital images were applied in the SAM classification in order to map the ...
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This research was performed with the objective of evaluating the accuracy of spectral angle mapper (SAM) classification using different reference spectra. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital images were applied in the SAM classification in order to map the distribution of hydrothermally altered rocks in the Kerman Cenozoic magmatic arc (KCMA), Iran. The study area comprises main porphyry copper deposits such as Meiduk and Chahfiroozeh. Collecting reference spectra was considered after pre-processing of ASTER VNIR/SWIR images. Three types of reference spectra including image, USGS library, and field samples spectra were used in the SAM algorithm. Ground truthing and laboratory studies including thin section studies, XRD analysis, and VNIR-SWIR reflectance spectroscopy were utilized to verify the results. The accuracy of SAM classification was numerically calculated using a confusion matrix. The best accuracy of 74.01% and a kappa coefficient of 0.65 were achieved using the SAM method using field samples spectra as the reference. The SAM results were also validated with the mixture tuned matched filtering (MTMF) method. Field investigations showed that more than 90% of the known copper mineralization occurred within the enhanced alteration areas.
