Z. Gharehchahi; M. Moayyed; J. Ahmadiyan; M. Murata
Abstract
Sungun and Haftcheshme mines located in Arasbaran volcano-plutonic complex in the North West of Azarbaijan province. lamprophyric dikes of Sungun with younger age Oligo-Miocene including minerals, biotite, plagioclase and pyroxene, porphyritic texture, classified part of the calc-alkaline and kersantite ...
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Sungun and Haftcheshme mines located in Arasbaran volcano-plutonic complex in the North West of Azarbaijan province. lamprophyric dikes of Sungun with younger age Oligo-Miocene including minerals, biotite, plagioclase and pyroxene, porphyritic texture, classified part of the calc-alkaline and kersantite and The area lamprophyric rocks are intruded in to Sungun quartz - monzonite. lamprophyric dikes of haftcheshme with with younger age Oligo-Miocene including minerals, biotite, alkali feldspar and pyroxene, the main textures microlitic porphyy and flow, Calc-alkaline Lamprophyre component - are classified minette and The area lamprophyric rocks are intruded in to Haftcheshme quartz - diorite. The behavior of trace elements studied by the means of spider diagrams show low enrichment of LREE relative to MREE and HREE. Review geochemical characteristics of the study show that the origin of mantle-derived magmas essentially lamprophyres. The lamprophyric rocks are generated from calc-alkaline lamprophyrecomponent, related to magmatic arc tectonic setting and these lamprophyres have gneated from lamprophyre low degree of partial melting of garnet lherzolite.
A Kamali; M Moayyed; N Amel; M.R Hosainzadeh
Abstract
Sungun Cu-Mo porphyry deposit is located in East Azarbaijan province and at northwest of Iran. From the petrology viewpoint, the Sungun copper mine is consisted of porphyry Sungun (SP), and eight categories of delayed dykes made of quartz-diorite (DK1 (a, b, c)), gabbro-diorite (DK2), diorite (DK3), ...
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Sungun Cu-Mo porphyry deposit is located in East Azarbaijan province and at northwest of Iran. From the petrology viewpoint, the Sungun copper mine is consisted of porphyry Sungun (SP), and eight categories of delayed dykes made of quartz-diorite (DK1 (a, b, c)), gabbro-diorite (DK2), diorite (DK3), dacite (DK4), lamprophyre (LAM) and micro-diorite (MDI). The main minerals of lamprophyric dyke are biotite, plagioclase, K-feldspar, and amphibole with porphyritic and microlithic porphyre textures. Lamprophyric dykes in the studied area have alkali-basalt composition and based on whole rock geochemistry is originated from a shoshonitic magma. Minerals chemistry analysis revealed that the composition of plagioclase varies from oligoclase to albite, amphibole is Magnesiohornblende and biotite composition varies from siderophyllite to eastonite. Lamprophyric dykes have been originated from a magma with high oxygen fugacity. Based on biotite thermometry, the temperature of biotite crystallization in lamprophyric dyke was 650 to 750°C. According to the mineralogical and geochemical evidence, studied lamprophyre samples are of kersantite type and belong to calk-alkaline lamprophyres. Multi-element diagrams normalized to chondrite and primitive mantle indicates LREE and LILE enrichment and HREE and HFSE depletion in the lamprophyre dykes Sungun. Based on trace elements ratio diagram of La/Sm vs. La parental magmas can have been generated from low degree partial melting of subcontinental mantle source with garnet-lherzolite composition. The dykes formed in Post-collisional geotectonic environment of the studied samples and trace element geochemical evidence indicate that produced magma formed from a metasomatic mantle due to an ancient subduction.
M. Mohamadian; M. Boomeri; A. Ahmadi; A. A. Moridi Farimani; H. Daglas; M. Jamali
Abstract
Sungun Porphyry Copper Deposit is located about 130 km to the northeast of Tabriz, northeast Iran. This deposit contains 796 million tons of ore with 0.61% Cu, 0.01% Mo, 0.016 ppm Au, 9.75 ppm Bi and Re (0.09 wt% of molybdenite). This research studies the physico-chemical environment using the chemical ...
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Sungun Porphyry Copper Deposit is located about 130 km to the northeast of Tabriz, northeast Iran. This deposit contains 796 million tons of ore with 0.61% Cu, 0.01% Mo, 0.016 ppm Au, 9.75 ppm Bi and Re (0.09 wt% of molybdenite). This research studies the physico-chemical environment using the chemical properties of minerals of this deposit. This deposit is related to granodiorite-diorite dikes and granodiorite stock that intruded into sedimentary and volcanic rocks of Cretaceous and Eocene. Dominant alterations are potassic, phyllic, argillic and propylitic from center outward which are characteristics of continental margin porphyry copper deposits. Simultaneously with the supergene mineralization, an iron cap of iron oxides, iron hydroxides, copper oxides, sulfates, carbonates and copper phosphates were developed on top of the deposit and along the fractures. In conclusion, based on mineral chemistry, in 450 ºC and 0.5 kb pressure (PT condition of potassic zone), sulfur fugacity and pH is determined to be as: log¦S2=-18 to -24 an pH=2.5 to 7.5. In 350 ºC and 0.5 kb pressure (PT condition of phyllic zone), oxygen and sulfur fugacities are: logƒO2= -20 to -33 and log¦S2=-6 to -15.
A. Hezarkhani; P. Tahmasbi; O. Asghari
Abstract
Separation of alteration zones is one of the important processes in evaluation and identification of mining activities that provide great help to have better view of the region and its mineralization. Most of the alteration separation is based on petrological investigations and the other methods are ...
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Separation of alteration zones is one of the important processes in evaluation and identification of mining activities that provide great help to have better view of the region and its mineralization. Most of the alteration separation is based on petrological investigations and the other methods are less applied. Therefore, in this research, there is an attempt by applying RBPNN (Radial Basis Probabilistic Neural Network) to separate these alteration zones. Because of the special structure and easy designing of these networks, they are usually capable to solve the classification problem. The input data were 28 element analyses related to 45 geochemical samples and its outputs were classified alteration zones (potassic, transition, phyllic) that was coding for every inputs data. After selection the training and testing data, the network has been prepared for training and then the data were inputs and the results were outputs. According to the results, the network could distinguish the difficult spatial relation between the inputs, with 28 spatial variables and classify those correctly. The calculated MSE (Mean Square Error) is 0.0163, which shows the good performance of network in this field.
