porphyry copper deposit

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.

The Mineral Prospectivity Map (MPM) is a powerful tool for identifying target areas for the exploration of undiscovered mineral deposits. In this study, a knowledge-driven Index overlay technique was utilized to create the MPM on a regional scale. The complex distribution patterns of geological features associated with mineral deposits were mapped and correlations between these features and mineral deposits were revealed by integrating geological, geophysical, hydrothermal alteration, and fault density data layers. It was found that 23% of the study area was highly prospective, with 77% of the known porphyry copper occurrences located within this area. The normalized density was equal to 3.35, indicating a significant relationship between the known porphyry copper occurrences and their occupied area. The MPM also identified potential tracts outside the known mineralized areas that can be used for exploration and quantitative assessment of undiscovered resources. It is suggested that the MPM is a valuable tool for mineral exploration and could have significant implications for the mining industry.

Identification of geochemical anomalies is a critical task in mineral exploration targeting. Decades of research and technology have resulted in new algorithms and techniques for recognizing anomaly detection methods at various scales and sample media. However, algorithms cannot always reveal the true nature of geological processes. The mineral system concept may contribute to a better understanding of the geological processes required to form and preserve ore deposits at all spatial and temporal scales. The mineral systems concept investigates the geochemical processes occurring within mineral subsystems in soil samples from the porphyry prospect area. The Cu/(Al + Ca) index was used to compare Cu, Mo, and (Pb* Zn)/(Cu*Mo) to highlight the region of interest for mineral potential mapping and pioneer borehole drilling based on fluid-rock interaction and secondary processes (e.g., alteration, weathering, and leaching). Exploratory boreholes validate a better performing Cu/(Al + Ca) index for detecting and refining soil geochemical anomalies.

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.

Due to substantial effect of classification of resource models on future mine planning, one should come with an accurate method of estimation to guarantee that the minimum error is acquired in the estimation process. The known world class Cu-Mo deposit, Sarcheshmeh Porphyry deposit (central Iran) selected as the study area. The Hypogene zone of the deposit was chosen as the space in which estimation processes should be done. The mean value of Molybdenum and Copper extracted from the top part of this zone, where sampling operations have been done on a dense grid. The correlation coefficient of 0.45 allowed going through the process of interpolation. It was shown that taking account Cu as an auxiliary variable the interpolation process, the estimation had been improved. Simple Cokriging interpolation technique is applied and it was proved that using Cu, with mean value of 0.61 percent, as secondary variable will decrease the estimation variance of Mo interpolation which has the mean value of 0.022 percent. The chief influence of this reduction appeared when the resource should be classified. Only 1% decrease was obtained when Cu used as secondary variable, but in an industrial aspect it can be of great importance as a high number of voxels in “Indicated” class changed into “Measured” one. This led to 133 Mt more Mo-ore that were added to the previous “Measured” class blocks. Also, the transition zones where the changes in class of cells have occurred are identified; these zones are mainly the places where Mo has fewer samples than Cu.

Due to substantial effect of classification of resource models on future mine planning, one should come with an accurate method of estimation to guarantee that the minimum error is acquired in the estimation process. The known world class Cu-Mo deposit, Sarcheshmeh Porphyry deposit (central Iran) selected as the study area. The Hypogene zone of the deposit was chosen as the space in which estimation processes should be done. The mean value of Molybdenum and Copper extracted from the top part of this zone, where sampling operations have been done on a dense grid. The correlation coefficient of 0.45 allowed going through the process of interpolation. It was shown that taking account Cu as an auxiliary variable the interpolation process, the estimation had been improved. Simple Cokriging interpolation technique is applied and it was proved that using Cu, with mean value of 0.61 percent, as secondary variable will decrease the estimation variance of Mo interpolation which has the mean value of 0.022 percent. The chief influence of this reduction appeared when the resource should be classified. Only 1% decrease was obtained when Cu used as secondary variable, but in an industrial aspect it can be of great importance as a high number of voxels in “Indicated” class changed into “Measured” one. This led to 133 Mt more Mo-ore that were added to the previous “Measured” class blocks. Also, the transition zones where the changes in class of cells have occurred are identified; these zones are mainly the places where Mo has fewer samples than Cu.