جستجوی مقالات مرتبط با کلیدواژه « اکتشاف مواد معدنی » در نشریات گروه « زمین شناسی »

In this research, to contribute to the understanding of the geochemistry of trace elements in zircon, we determined the REEs, Y, Nb, Ta, Th, and U contents in zircon grains in three granitoids (Sarnowsar, Sarkhar and Bermani) by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) in order to determining the magma sourcee and magmatism fertility. The zircon/rock partitioning coefficients of REE, Y, Nb, Ta, Th, and U contents indicate that the patterns of the trace elements of the studied granitoids are controlled by the liquid composition at the magmatic crystallization. Compared to Sarkhar and Bermani granitoids, the Sarnowsar granite has a higher temperature (736° to 915 °C), higher zircon/rock partitioning coefficient of REEs, Y and Th (up to 2770 for Zr) and it was formed in higher oxidant conditions (△FMQ values between -0.06 to 17.01). The results of this study show that oxidized and I-type magmas in subduction-related tectonic environments are more favorable for porphyry and skarn mineralization.

Zircon is the most commonly analyzed accessory mineral and is routinely employed in U–Th–Pb geochronology, (U–Th)/He and fission track thermochronology, radiogenic (Hf) and stable (O) isotopic studies, crystallization thermometry, and trace element geochemistry (Belousova et al., 2002). A critical presumption in zircon chemistry studies is that data obtained from zircon is a proxy for the parent igneous rock. This is evidently true for most types of analyses, however, relating trace element and rare earth element (REE) concentrations in zircon compared to bulk rock or melt concentrations has been verified for causing some difficulties. The incentive to establish more accurate estimates of parental bulk rock concentrations using in-situ zircon measurements is significant as it would link zircon to a large body of whole rock geochemical literature with numerous possible applications including studies of magma source, crustal thickness, mineral exploration, crustal evolution, metamorphism, and petrogenesis (Chapman et al., 2016 and references therein). In this research, we determined the contents of REEs, Y, Nb, Ta, Hf, U, and Th in the zircon grains of eight granitoid samples from the Sangan mining district, NE Iran, to calculate zircon/rock partitioning coefficients of REEs applicable to magma source studies and mineral exploration.

Zircon from Sarkhar and Bermani granitoids were analyzed at the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, using laser ablation system, ICP-MS instrument (Agilent 7700a ICP-MS). Also, samples from Sarnowsar granitoids were analyzed at the Nanjing Hongchuang Geological Exploration Technology Service Co. Ltd., China. Zircons were analyzed for trace elements using a laser energy density of 3.6 J/cm2, a spot size of 30 μm, and a repetition rate of 5 Hz.
The estimations of melt composition and the measured trace element concentrations in zircon, values of DCezircon/rock was calculated. Estimates for DCe3+zircon/rock and DCe4+zircon/rock were implemented after Ballard et al. (2002) method. Partition coefficients of the trivalent REEs and the quadrivalent series Hf, Th, and U are used to constrain DCe3+zircon/rock and DCe4+zircon/rock, respectively. Blundy and Wood (1994) showed that the mineral melt partition coefficient for a cation i can be related to the lattice strain energy created by substituting a cation, whose ionic radius (ri) differs from the optimal value for that site (r0) (Equation 1).
lnDi=lnD0-4π ENA/RT (ri/3+r0/6) (ri- r0)2                                                                                     (1)
Plotting lnDi against the (ri/3 +r0/6)(ri−r0)2 yields a linear relation for an isovalent series of cations. With knowing the ionic radii of Ce3+ and Ce4+, partition coefficients of these species can be determined by interpolation. Since Ce will be a mixture of Ce3+ and Ce4+, the value of DCezircon/rock will lie between these two-partition coefficient end-members, and by combining Equations (1) and (2) oxygen fugacity fO2 of crystallization can be estimated.
ln[xmelt Ce4+/ xmelt Ce3+]= 1/4 ln fO2 + 13136 (±591)/T − 2.064 (±0.011) NBO/T −8.878(±0.112).xH2O −8.955 (±0.091)                                                                                                                                              (2)
Temperatures were calculated using the Ti content of zircon, by using the Equation (3) (Ferry and Watson, 2007):log(Tizircon) = (5.711 ± 0.072) – 4800 ± 86/T −logaSiO2 + logaTiO2                                                     (3)
where Tizircon is the concentration of Ti in zircon in ppm, T is temperature degrees in Kelvin, and ai (aSiO2 and aTiO2) is the ratio of component i concentration in the melt over the concentration of component i in the rock at saturation.

Concentrations of trace elements in the zircon grains are given in the Supplementary Table and are summarized in Table 1, where the geometric mean (G), the variation coefficient CV (which corresponds to the ratio of standard deviation by the arithmetic mean), and the number of determinations (n) are listed. Heavy rare earth elements (HREEs) show a relative enrichment. (Lu)N in the Sarnowsar granitoids range from 1522.40 to 8869.00 (DA sample), 1477.00 to 6442.00 (TP sample) and 1655.37 to 53.00 7523 (CSK sample). These values for Sarkhar and Bermani granitoids are in the range of 835.43 to 4077.95 (BR-01 sample), 1233.46 to 4337.80 (SK-01 sample), 1984.37 to 5681.81 (SK-1-1 sample), 2281.71 to 4955.97 (SK-1-2 sample) and 2268.68 to 6422.57 (SK-2-2 sample). Furthermore, the LREEs show lower concentrations, resulting in an (La/Yb)N that is generally less than 0.1. A negative Eu anomaly and a positive Ce anomaly are observed in studied granitoids. Contents and patterns of REEs in the studied granite samples are similar to those reported by other authors with higher HREEs contents than LREEs (e.g., Hoskin and Schaltegger, 2003).
Zircon Ti concentrations used to constrain its crystallization temperatures, and T is calculated by the revised Ti-in-zircon thermometry of Ferry and Watson (2007) (Supplementary Table). Calculated Ti-in-zircon temperatures for Sarnowsar intrusions (736° to 915 °C) are higher than those of Sarkhar (646° to 819 °C) and Bermani granitoids (653° to 861 °C). The △FMQ values (calculated by equation of Trail et al., 2011; Trail et al., 2012) for Sarnowsar range from -0.06 to 17.01. For Sarkhar and Bermani intrusions, the △FMQ values are in the range -4.43 to 4 and -8.53 to 0.07, respectively. These data indicating different magmatic conditions for productive (Sarnowsar) and barren (Sarkhar and Bermani) granitoids.

Defining the magma source and fertility of acidic to intermediate magmatism within the Cenozoic volcano-plutonic magmatic belts of Iran has immense scientific and mineral exploration significance. By using the zircon/whole rock partition coefficient in the Sangan mining area (Sarnowsar, Sarkhar and Bermani granitoids), we discuss the magma source and fertility of magmatism. The magmatism of Sangan mining district area is alkaline and classified as I-type granitoids, which have a direct genetic association with iron skarn mineralization in the area. The current results show that Sarnowsar granitoid was formed at a higher temperature (736° to 915 °C), lower negative Eu anomaly (with geometric mean 0.35 to 0.4), higher values ​​of Ce4+/Ce3+ (25.56 to 718.62) ratios compared to Sarkhar and Bermani granitoids. Also, the Sarnowsar granitoid has lower Hf values ​​(700 to 1199 ppm) and higher Zr/Hf (65 to 87) values, which shows that it formed in an earlier magmatic stage compared to Sarkhar and Bermani granitoids. The results of this research can be used for identifying productive Cenozoic intrusions in Iran that are related to porphyry and skarn mineralization systems.

IntroductionThe growing demand for base metals such as iron, copper, lead and zinc on the one hand and the diminishing of surficial and shallow resources of these elements on the other hand have forced explorationists to focus on detecting deep deposits of these metals. As a result, the discovery of such deep deposits requires more advanced and sophisticated methods in the course of preliminary prospecting stages. Since the discovery of new deposits is getting to be increasingly difficult, deploying new prospecting technologies that employ more deposit attributes in the course of combining exploratory evidence may reduce the exploration costs with lower uncertainties. In the past two decades, a number of new data mining and integrating approaches capable of incorporating direct and indirect mineralization indicators, based on expert knowledge, data, or a combination of both, have been proposed )Bonham-Carter, 1994(. In the first step, the input exploratory data layers are corrected and validated through applying some statistical pre-processing algorithms such as background and outlier removal methods. In order to detect a mineralization occurrence, it is necessary to find the proper exploratory geological, geochemical and geophysical data layers which have direct or indirect associations with the governing mineralization followed by constructing these models in an appropriate GIS platform (Malkzewski, 1999). Due to the imperfect available data and a number of unknown parameters affecting the mineralization process, the application of conventional GIS integration methods such as Boolean or weighted overlay or even fuzzy logic methods alone may not produce appropriate results, pointing to a need for deploying multi-criteria decision-making methods such as TOPSIS. In the present study, the pre-processed exploratory data including geological, remotely sensed geophysical and geochemical imagery were used to detect favorable mineralization zones through applying the multi-criteria decision-making method. Finally, the selected favorable areas in the metallogenic strip located at the south to the south-east of the Sarcheshmeh porphyry copper deposit are prioritized and introduced for further follow up ground exploration operations.
MethodologyIn order to solve complex decision-making problems like the problem of mapping favorable porphyry copper mineralization zones under great uncertainties, the TOPSIS method is considered as an appropriate approach offering significant simplicity, flexibility and capability (Ataei., 2010). The TOPSIS method is considered to be an efficient method due to having very high accuracy, speed, sensitivity as well as being easy to implement and interpret the outputted results (Hwang and Yoon, 1981). It has found many applications in important areas of mining industry where there is a need to make decisions under risky conditions and data uncertainties.
One basic issue in applying decision-making methods in the field of mineral exploration is to rank and propose the best possible candidates among all potentially favorable areas for the next stage of mineral exploration. In this regard, the best favorable areas are selected based on exploratory data layers including favorable lithologies, alterations, structures plus geochemical and geophysical anomalies (Pazand et al., 2012).
Results and discussionIn the first step, the area located south to the southeast of one the largest porphyry copper deposits in Iran known as Sarcheshmeh was investigated for favorable areas using all available exploratory data as mentioned in the previous section using fuzzy logic integration approach in the GIS environment.
Evaluating the highly favorable areas presented by the fuzzy logic approach showed great consistency with the already known copper mineralization prospects. Next, the first 20 priorities obtained from the fuzzy logic approach were chosen as the best candidates to be ranked using the TOPSIS multi criteria decision-making method. Among these favorable prospects, the one with the highest coefficient close to the ideal solution of 0.796 was found to be coincident with the Darehzar area that is a well known porphyry copper deposit 12 kilometers south of the Sarcheshmeh deposit.
The favorable areas numbered 5 and 8 that correspond to well known porphyry copper mineralization prospects called Sereydoon and North Sereydoon were ranked as the second and third priorities with scores of 0.721 and 0.604, respectively. Other favorable areas ranked by the TOPSIS method were also prioritized and presented for further follow up explorations.
To assess the sensitivity of the results obtained by the TOPSIS method, an amount of 10% of the values of each of the criteria were added and the outputted ranking results were compared to that of the original TOPSIS results. It was concluded that a slight change in the values of the criteria would not have significant impact on the results. However, 10 percent change of each criteria weight would greatly affect the prospects priorities obtained by re-applying the TOPSIS method.