Sulfide ore facies, fluid inclusion and sulfur isotope characteristics of the Tappehsorkh Zn-Pb (± Ag-Ba) deposit, South Esfahan, Iran

Konari, Mina Boveiri; Rastad, Ebrahim; Peter, Jan M.; Choulet, Flavien; Kalender, Leyla; Nakini, A

doi:10.1016/j.chemer.2020.125600

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…The Gushfil Lead–Zinc mine is located in the Irankuh Mining District, 20 km South West of Esfahan city in Iran. The Irankuh Mining District (IMD) is a part of the Malayer-Esfahan Metallogenic Belt between Sanandaj and Sirjan cities 1 . It has been proposed that the IMD is an epigenetic Pb, Zn, and Ba Mississippi Valley-Type (MVT) according to isotopic investigations 2 .…”

Section: Introductionmentioning

confidence: 99%

Critical importance of pH and collector type on the flotation of sphalerite and galena from a low-grade lead–zinc ore

Foroutan¹,

Abadi²,

Kianinia³

et al. 2021

Sci Rep

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Collector type and pulp pH play an important role in the lead–zinc ore flotation process. In the current study, the effect of pulp pH and the collector type parameters on the galena and sphalerite flotation from a complex lead–zinc–iron ore was investigated. The ethyl xanthate and Aero 3418 collectors were used for lead flotation and Aero 3477 and amyl xanthate for zinc flotation. It was found that maximum lead grade could be achieved by using Aero 3418 as collector at pH 8. Also, iron and zinc recoveries and grades were increased in the lead concentrate at lower pH which caused zinc recovery reduction in the zinc concentrate and decrease the lead grade concentrate. Furthermore, the results showed that the maximum zinc grade and recovery of 42.9% and 76.7% were achieved at pH 6 in the presence of Aero 3477 as collector. For both collectors at pH 5, Zinc recovery was increased around 2–3%; however, the iron recovery was also increased at this pH which reduced the zinc concentrate quality. Finally, pH 8 and pH 6 were selected as optimum pH values for lead and zinc flotation circuits, respectively.

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Section: Introductionmentioning

confidence: 99%

Critical importance of pH and collector type on the flotation of sphalerite and galena from a low-grade lead–zinc ore

Foroutan¹,

Abadi²,

Kianinia³

et al. 2021

Sci Rep

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“…One of the essential features in the IMD is the WNW-ESE trending Gushfil-Baghabrisham fault, which was common in the Lower Cretaceous (contemporaneous with the formation of a back-arc basin and sulfide deposition) but changed to reverse fault due to inversion tectonic of Late Cretaceous compression tectonism [61]. Based on [62][63][64], this fault is the main conduit controlling the movement of ore-bearing fluids that deposited economic stratiform and stratabound sulfide minerals in the proximal host rocks.…”

Section: Geological Settingmentioning

confidence: 99%

“…Based on the geology, tectonic setting, mineralogy, ore texture, geochemical, and isotopic analytical results, the IMD and included ore deposits have been classified as sub-seafloor diagenetic replacement SEDEX-type deposits [62][63][64]. Karimpour and Sadeghi [65,66], based on the deposition of sulfide minerals within or close to dolostone and shale-siltstone units and replacement and open space filling textures of ore minerals, suggested this deposit occurred as an MVT-type deposit.…”

Section: Geological Settingmentioning

confidence: 99%

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Farhadi

Afzal

Konari³

et al. 2022

Minerals

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Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements' main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution.

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Solid bitumen reflectance and authigenic clay mineralogy in relation to the Irankuh ore deposition: An innovative approach to basin analysis

Hosseini

Swennen

Mahboubi

et al. 2022

Journal of African Earth Sciences

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Sulfide ore facies, fluid inclusion and sulfur isotope characteristics of the Tappehsorkh Zn-Pb (± Ag-Ba) deposit, South Esfahan, Iran

Cited by 5 publications

References 36 publications

Critical importance of pH and collector type on the flotation of sphalerite and galena from a low-grade lead–zinc ore

Critical importance of pH and collector type on the flotation of sphalerite and galena from a low-grade lead–zinc ore

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Solid bitumen reflectance and authigenic clay mineralogy in relation to the Irankuh ore deposition: An innovative approach to basin analysis

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