Structural controls on the localization of Cu deposits in the Kerman Cu metallogenic province of Iran using geoinformatic techniques

Safari, Hojjat Ollah; Bagas, Leon; Bafti, Behnam Shafiei

doi:10.1016/j.oregeorev.2014.11.014

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…2014; Shafiei Bafti and Mohajjel, 2015). This tectonic event that prepared the suitable sites for emplacement of the granitic plutons and associated Cu deposits in a broad fault zone occurred during the emplacement of the Syn-to post Miocene granitic bodies into the volcanic and volcano-sedimentary sequences (Shafiei et al, 2009;Safari et al, 2015). Similar structural model that presented here for Sarduiyeh area, the fault duplexes as a major structure have been identified in the other Cu belts including the central Europe (Drew, 2005), Chuquicamata (Sibson, 1987), and Escondido deposits in northern Chile (Richards et al, 2001;Richards, 2003).…”

Section: Fault Kinematic Data and Structural Modelsupporting

confidence: 74%

“…Evaluation of structural controls on Cu deposits has been investigated by structural geologists and covers a large part of applied structural geology to exploration and exploitation (Richards et al, 2001). Structural analysis in the main Cu belts revealed that the localization of the intrusions and associated Cu deposits are controlled by transpression (Cox, 1986;Cox et al, 1986;Tosdal and Richards, 2001;Safari et al, 2015). In this tectonic process, slip partitioning to the dipslip and strike-slip movements cause various structural domains such as fold-thrust belt and strike-slip domain on the overriding plate.…”

Section: Introductionmentioning

confidence: 99%

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Controls on Cu mineralization in central part of the Kerman porphyry copper belt, SE Iran:constraints from structural and spatial pattern analysis

Amirihanza

Shafieibafti

Derakhshani

et al. 2018

Journal of Structural Geology

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Section: Fault Kinematic Data and Structural Modelsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Controls on Cu mineralization in central part of the Kerman porphyry copper belt, SE Iran:constraints from structural and spatial pattern analysis

Amirihanza

Shafieibafti

Derakhshani

et al. 2018

Journal of Structural Geology

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“…Some of the lithological units such as Oligocene granite and granodiorite, and Miocene-Pliocene andesite are the host rocks for porphyry copper mineralization which could determine possible copper mineralization within the study region. Faults are the main conduits of ascending fluids, and they provide porous zones in which metals can be trapped (Safari et al 2015;Mirzaie et al 2015). A maximum distance of 1000 m from a fault has been reported for copper mineralization within the study region (Derakhshani and Mchrabi 2009).…”

Section: Introductionmentioning

confidence: 90%

Evaluation of the modified AHP-VIKOR for mapping and ranking copper mineralized areas, a case study from the Kerman metallogenic belt, SE Iran

Moud

Shahri²,

Ruitenbeek

et al. 2022

Arab J Geosci

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Exploration and prioritization of potentially mineralized areas by integrating different geoscientific datasets help to manage the time and costs of an exploration project. In this study, copper exploration and prioritization of explored areas were done on the Kerman copper belt, which was formed due to the subduction of the Arabian plate under the Iranian plate. After processing geoscientific layers (geology, hydrothermal alteration zones, stream sediment geochemistry, and airborne geophysics), the analytic hierarchy process (AHP) method was used to compute the weights of geoscientific layers by a decision-making group consisting of ten local experts in copper exploration. The weight of evidence (WofE) method was used to compare and assess the fairness of the AHP weights. The AHP weights were used for the multi-criteria optimization and compromise solution (VIKOR) method to integrate the geoscientific layers and create an AHP-VIKOR copper mineralization potential map. The “v” parameter of the VIKOR method was modified by replacing the concept of mathematical expectation value with the manual assignment. The real value-area (RV-A) fractal method was used to select optimum threshold values for different AHP-VIKOR copper mineralization potential classes. The accuracy of the classified AHP-VIKOR copper mineralized potential map was assessed by creating a confusion matrix and using existing mineralization locations. The average pixel values of the copper mineralization class were extracted and sorted to rank the very high potential areas. The AHP-VIKOR ranking results were compared with recorded ore grade and ore tonnage information of each copper deposition. The results demonstrated that the AHP ranks are roughly similar to the WofE ranks, except for lithology. An accuracy of 84% for classified copper mineralization potential map with the RV-A fractal method was obtained. The used expectation value for the “v” parameter of the VIKOR method showed that the decision-making group’s opinion was more important than the individual opinions (v = 0.6). The AHP-VIKOR method identified mineralization areas, especially porphyry deposits. Also, the outcome of the AHP-VIKOR ranking showed that the currently active mines, especially porphyry deposits, have the highest priority for mining. This study showed that the AHP-VIKOR approach helps explore and rank favorable areas with high copper ore grade and tonnage characteristics.

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“…The Iju area, within 30 • 31 45 N to 30 • 33 05 N and 54 • 56 10 E to 54 • 57 30 E (Figure 1), is located 42 km NW of Shahre-Babak county, Kerman province, and 140 km NW of the Sarcheshme Cu mine. The Iju deposit is situated on the SE part of the Urmia-Dokhtar magmatic belt [41][42][43], which is characterized by numerous Cu deposits such as the Chah-Firouze, Sarcheshme, Meiduk, and Chah-Messi (Figure 1).…”

Section: Geologymentioning

confidence: 99%

“…Sarcheshme Cu mine. The Iju deposit is situated on the SE part of the Urmia-Dokhtar magmatic belt [41][42][43], which is characterized by numerous Cu deposits such as the Chah-Firouze, Sarcheshme, Meiduk, and Chah-Messi (Figure 1). The Iju deposit is situated in a mountainous area with Eocene-Paleocene pyroclastic volcanic rocks intruded by Miocene quartz-diorite and tonalite [44] (Figure 1).…”

Section: Geologymentioning

confidence: 99%

Geostatistical Evaluation of a Porphyry Copper Deposit Using Copulas

Sohrabian,

Soltani-Mohammadi,

Pourmirzaee

et al. 2023

Minerals

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Kriging has some problems such as ignoring sample values in giving weights to them, reducing dependence structure to a single covariance function, and facing negative confidence bounds. In view to these problems of kriging in this study to estimate Cu in the Iju porphyry Cu deposit in Iran, we used a convex linear combination of Archimedean copulas. To delineate the spatial dependence structure of Cu, the best Frank, Gumbel, and Clayton copula models were determined at different lags to fit with higher-order polynomials. The resulting Archimedean copulas were able to describe all kinds of spatial dependence structures, including asymmetric lower and upper tails. The copula and kriging methods were compared through a split-sample cross-validation test whereby the drill-hole data were divided into modeling and validation sets. The cross-validation showed better results for geostatistical estimation through copula than through kriging in terms of accuracy and precision. The mean of the validation set, which was 0.1218%, was estimated as 0.1278% and 0.1369% by the copula and kriging methods, respectively. The correlation coefficient between the estimated and measured values was higher for the copula method than for the kriging method. With 0.0143%2 and 0.0162%2 values, the mean square error was substantially smaller for copula than for kriging. A boxplot of the results demonstrated that the copula method was better in reproducing the Cu distribution and had fewer smoothing problems.

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Structural controls on the localization of Cu deposits in the Kerman Cu metallogenic province of Iran using geoinformatic techniques

Cited by 12 publications

References 29 publications

Controls on Cu mineralization in central part of the Kerman porphyry copper belt, SE Iran:constraints from structural and spatial pattern analysis

Controls on Cu mineralization in central part of the Kerman porphyry copper belt, SE Iran:constraints from structural and spatial pattern analysis

Evaluation of the modified AHP-VIKOR for mapping and ranking copper mineralized areas, a case study from the Kerman metallogenic belt, SE Iran

Geostatistical Evaluation of a Porphyry Copper Deposit Using Copulas

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