Document Type : Original Research Paper
Authors
1 Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran
2 School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Faculty of Engineering, Malayer University, Malayer, Iran
Abstract
Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were employed in alignment with the conceptual model of these deposits. These layers encompass fault density, proximity to intrusive rocks, multi-element geochemical anomalies, and distances to phyllic and argillic alterations. The geochemical anomaly maps, recognized as the most effective layers, were generated through staged factor analysis (SFA) and the geochemical mineralization probability index (GMPI). Other layers were weighted using a logistic function, and their values were transformed into 0 -1 interval. Ultimately, to integrate the weighted layers, the fuzzy gamma operator and the geometric average method were applied. The normalized density index and prediction-area (P-A) plot were employed to evaluate the MPM models. The findings indicate that the developed models possess considerable validity and can be effectively utilized for planning future exploration endeavors.
Keywords
Main Subjects
[1]. Ghezelbash, R., Maghsoudi, A., Daviran, M., & Yilmaz, H. (2019). Incorporation of principal component analysis, geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district, NW Iran. Geochemistry, 79(2), 323-336.
[2]. Kashani, S. B. M., Abedi, M., & Norouzi, G. H. (2016). Fuzzy logic mineral potential mapping for copper exploration using multi-disciplinary geo-datasets, a case study in seridune deposit, Iran. Earth Science Informatics, 9, 167-181.
[3]. Parsa, M. (2021). A data augmentation approach to XGboost-based mineral potential mapping: an example of carbonate-hosted ZnPb mineral systems of Western Iran. Journal of Geochemical Exploration, 228, 106811.
[4]. Daviran, M., Maghsoudi, A., Cohen, D. R., Ghezelbash, R., & Yilmaz, H. (2020). Assessment of various fuzzy c-mean clustering validation indices for mapping mineral prospectivity: combination of multifractal geochemical model and mineralization processes. Natural Resources Research, 29, 229-246.
[5]. Zhang, S., Carranza, E. J. M., Xiao, K., Wei, H., Yang, F., Chen, Z., ... & Xiang, J. (2022). Mineral prospectivity mapping based on isolation forest and random forest: Implication for the existence of spatial signature of mineralization in outliers. Natural Resources Research, 31(4), 1981-1999.
[6]. Hajihosseinlou, M., Maghsoudi, A., & Ghezelbash, R. (2024). Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran. Expert Systems with Applications, 237, 121668.
[7]. Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019). Performance evaluation of RBF-and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of SA multifractal model and mineralization controls. Earth Science Informatics, 12(3), 277-293.
[8]. Yousefi, M., & Hronsky, J. M. (2023). Translation of the function of hydrothermal mineralization-related focused fluid flux into a mappable exploration criterion for mineral exploration targeting. Applied Geochemistry, 149, 105561.
[9]. Yousefi, M., Lindsay, M. D., & Kreuzer, O. (2024). Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS). Ore Geology Reviews, 105930.
[10]. Yousefi, M., & Nykänen, V. (2016). Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. Journal of Geochemical Exploration, 164, 94-106.
[11]. Ghezelbash, R., & Maghsoudi, A. (2018). Application of hybrid AHP-TOPSIS method for prospectivity modeling of Cu porphyry in Varzaghan district, Iran. Scientific Quarterly Journal of Geosciences, 28(109), 33-42.
[12]. Shabani, A., Ziaii, M., Monfared, M. S., Shirazy, A., & Shirazi, A. (2022). Multi-Dimensional Data Fusion for Mineral Prospectivity Mapping (MPM) Using Fuzzy-AHP Decision-Making Method, Kodegan-Basiran Region, East Iran. Minerals, 12(12), 1629.
[13]. Ghadiri-Sufi, E., & Yousefi, M. (2016). Combination of data-and knowledge-driven fuzzy approaches in mineral potential modeling for generating target areas. Scientific Quarterly Journal of Geosciences, 25(98), 11-18.
[14]. Harris, J. R., Grunsky, E., Behnia, P., & Corrigan, D. (2015). Data-and knowledge-driven mineral prospectivity maps for Canada's North. Ore Geology Reviews, 71, 788-803.
[15]. Agha Seyyed Mirzabozorg, S. A., Abedi, M., & Ahmadi, F. (2023). Clustering of Areas Prone to Iron Mineralization in Esfordi Range based on a Hybrid Method of Knowledge-and Data-Driven Approaches. Journal of Mineral Resources Engineering, 8(4), 1-26.
[16]. Farhadi, S., Tatullo, S., Konari, M. B., & Afzal, P. (2024). Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping. Journal of Geochemical Exploration, 260, 107441.
[17]. Lou, Y., & Liu, Y. (2023). Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China. Earth and Space Science, 10(10), e2022EA002596.
[18]. Ghezelbash, R., & Esmailzadeh, M. (2021). Evaluation of machine learning algorithms for spatial predictive modeling of Au prospectivity. Construction science and technology, 1(4), 53-64.
[19]. Pourgholam, M. M., Afzal, P., Adib, A., Rahbar, K., & Gholinejad, M. (2024). Recognition of REEs anomalies using an image Fusion fractal-wavelet model in Tarom metallogenic zone, NW Iran. Geochemistry, 84(2), 126093.
[20]. Mokhtari, M., Hoseinzade, Z., & Shirani, K. (2020). A comparison study on landslide prediction through FAHP and Dempster–Shafer methods and their evaluation by P–A plots. Environmental Earth Sciences, 79, 1-13.
[21]. Rahimi, H., Abedi, M., Yousefi, M., Bahroudi, A., & Elyasi, G. R. (2021). Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof. Applied Geochemistry, 128, 104940.
[22]. Bigdeli, A., Maghsoudi, A., & Ghezelbash, R. (2024). A comparative study of the XGBoost ensemble learning and multilayer perceptron in mineral prospectivity modeling: a case study of the Torud-Chahshirin belt, NE Iran. Earth Science Informatics, 17(1), 483-499.
[23]. Abedi, M., Kashani, S. B. M., Norouzi, G. H., & Yousefi, M. (2017). A deposit scale mineral prospectivity analysis: A comparison of various knowledge-driven approaches for porphyry copper targeting in Seridune, Iran. Journal of African Earth Sciences, 128, 127-146.
[24]. Riahi, S., Bahroudi, A., Abedi, M., Lentz, D. R., & Aslani, S. (2023). Application of data-driven multi-index overlay and BWM-MOORA MCDM methods in mineral prospectivity mapping of porphyry Cu mineralization. Journal of Applied Geophysics, 213, 105025.
[25]. Sabbaghi, H., & Tabatabaei, S. H. (2023). Data-driven logistic function for weighting of geophysical evidence layers in mineral prospectivity mapping. Journal of Applied Geophysics, 212, 104986.
[26]. Yousefi, M., Kamkar-Rouhani, A., & Carranza, E. J. M. (2012). Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. Journal of Geochemical Exploration, 115, 24-35.
[27]. Daviran, M., Maghsoudi, A., Ghezelbash, R., & Pradhan, B. (2021). A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach. Computers & Geosciences, 148, 104688.
[28]. Mirzabozorg, S. A. A. S., & Abedi, M. (2023). Recognition of mineralization-related anomaly patterns through an autoencoder neural network for mineral exploration targeting. Applied Geochemistry, 158, 105807.
[29]. Saremi, M., Yousefi, M., & Yousefi, S. (2024). Separation of geochemical anomalies related to hydrothermal copper mineralization using staged factor analysis in Feizabad geological map.
[30]. Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saein, L., & Sadeghi, B. (2022). Combination of machine learning algorithms with concentration-area fractal method for soil geochemical anomaly detection in sediment-hosted Irankuh Pb-Zn deposit, Central Iran. Minerals, 12(6), 689.
[31]. Ghasemzadeh, S., Maghsoudi, A., Yousefi, M., & Mihalasky, M. J. (2022). Information value-based geochemical anomaly modeling: A statistical index to generate enhanced geochemical signatures for mineral exploration targeting. Applied Geochemistry, 136, 105177.
[32]. Bigdeli, A., Maghsoudi, A., & Ghezelbash, R. (2022). Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in Moalleman district, NE Iran. Journal of Geochemical Exploration, 233, 106923.
[33]. Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019). Mapping of single-and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models. Journal of Geochemical Exploration, 199, 90-104.
[34]. Yousefi, M., Carranza, E. J. M., & Kamkar-Rouhani, A. (2013). Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling. Journal of Geochemical Exploration, 128, 88-96.
[35]. Maghsoudi, A., Yazdi, M., Mehrparto, M., & Vosoghi Abideni, M. (2009). Geochemical zonation in Mirkoh alimirza area, Arasbaran zone, NW Iran. Geochimica et Cosmochimica Acta Supplement, 73, A815.
[36]. Aghazadeh, M., Hou, Z., Badrzadeh, Z., & Zhou, L. (2015). Temporal–spatial distribution and tectonic setting of porphyry copper deposits in Iran: constraints from zircon U–Pb and molybdenite Re–Os geochronology. Ore geology reviews, 70, 385-406.
[37]. Hezarkhani, A., & Williams-Jones, A. E. (1998). Controls of alteration and mineralization in the Sungun porphyry copper deposit, Iran; evidence from fluid inclusions and stable isotopes. Economic Geology, 93(5), 651-670.
[38]. Zarasvandi, A., Rezaei, M., Sadeghi, M., Lentz, D., Adelpour, M., & Pourkaseb, H. (2015). Rare earth element signatures of economic and sub-economic porphyry copper systems in Urumieh–Dokhtar Magmatic Arc (UDMA), Iran. Ore geology reviews, 70, 407-423.
[39]. Jamali, H., Dilek, Y., Daliran, F., Yaghubpur, A., & Mehrabi, B. (2010). Metallogeny and tectonic evolution of the Cenozoic Ahar–Arasbaran volcanic belt, northern Iran. International Geology Review, 52(4-6), 608-630.
[40]. Ghezelbash, R., & Maghsoudi, A. (2018). Comparison of U-spatial statistics and C–A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district, NW Iran. Comptes Rendus Geoscience, 350(4), 180-191.
[41]. M. Mehrpartou, "Geological map of Varzaghan, scale 1: 1,000,000," Geological survey of Iran, 1993.
[42]. A. Maghsoudi, M. Yazdi, M. Mehrpartou, M. Vosoughi, and S. Younesi, "Porphyry Cu–Au mineralization in the Mirkuh Ali Mirza magmatic complex, NW Iran," Journal of Asian Earth Sciences, vol. 79, pp. 932-941, 2014.
[43]. Maghsoudi, A., Rahmani, M., & Rashidi, B. (2005). Gold deposits and indications of Iran.
[44]. Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2020). Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm. Computers & Geosciences, 134, 104335.
[45]. Ghezelbash, R., Maghsoudi, A., & Daviran, M. (2019). Prospectivity modeling of porphyry copper deposits: recognition of efficient mono-and multi-element geochemical signatures in the Varzaghan district, NW Iran. Acta Geochimica, 38, 131-144.
[46]. Bahri, E., Alimoradi, A., & Yousefi, M. (2021). Mineral Potential Modeling of Porphyry Copper Deposits using Continuously-Weighted Spatial Evidence Layers and Union Score Integration Method. Journal of Mining and Environment, 12(3), 743-751.
[47]. Hezarkhani, A. (2006). Petrology of the intrusive rocks within the Sungun porphyry copper deposit, Azerbaijan, Iran. Journal of Asian Earth Sciences, 27(3), 326-340.
[48]. Yousefi, M., Barak, S., Salimi, A., & Yousefi, S. (2023). Should geochemical indicators be integrated to produce enhanced signatures of mineral deposits? A discussion with regard to exploration scale. Journal of Mining and Environment, 14(3), 1011-1018.
[49]. Lowell, J. D., & Guilbert, J. M. (1970). Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Economic geology, 65(4), 373-408.
[50]. Pour, A. B., & Hashim, M. (2012). The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore geology reviews, 44, 1-9.
[51]. Yousefi, M., & Carranza, E. J. M. (2015). Geometric average of spatial evidence data layers: a GIS-based multi-criteria decision-making approach to mineral prospectivity mapping. Computers & Geosciences, 83, 72-79.
[52]. Bahri, E., Alimoradi, A., & Yousefi, M. (2023). Investigating the performance of continuous weighting functions in the integration of exploration data for mineral potential modeling using artificial neural networks, geometric average and fuzzy gamma operators. International Journal of Mining and Geo-Engineering, 57(4), 405-412.
[53]. Zhang, N., & Zhou, K. (2015). Mineral prospectivity mapping with weights of evidence and fuzzy logic methods. Journal of Intelligent & Fuzzy Systems, 29(6), 2639-2651.
[54]. Li, H., Li, X., Yuan, F., Jowitt, S. M., Zhang, M., Zhou, J., ... & Wu, B. (2020). Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian–Zhangbaling area, Anhui Province, China. Applied Geochemistry, 122, 104747.
[55]. Saremi, M., Yousefi, S., & Yousefi, M. (2024). Combination of Geochemical and Structural Data to Determine Exploration Target of Copper Hydrothermal Deposits in Feizabad District. Journal of Mining and Environment, 15(3), 1089-1101.
[56]. Ghasemzadeh, S., Maghsoudi, A., Yousefi, M., & Mihalasky, M. J. (2019). Stream sediment geochemical data analysis for district-scale mineral exploration targeting: Measuring the performance of the spatial U-statistic and CA fractal modeling. Ore Geology Reviews, 113, 103115.
[57]. Afzal, P., Mirzaei, M., Yousefi, M., Adib, A., Khalajmasoumi, M., Zarifi, A. Z., ... & Yasrebi, A. B. (2016). Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis. Journal of African Earth Sciences, 119, 139-149.
[58]. Yilmaz, H., Yousefi, M., Parsa, M., Sonmez, F. N., & Maghsoodi, A. (2019). Singularity mapping of bulk leach extractable gold and− 80# stream sediment geochemical data in recognition of gold and base metal mineralization footprints in Biga Peninsula South, Turkey. Journal of African Earth Sciences, 153, 156-172.
[59]. Hoseinzade, Z., Mokhtari, A. R., & Zekri, H. (2018). Application of radial basis function in the analysis of irregular geochemical patterns through spectrum-area method. Journal of Geochemical Exploration, 194. https://doi.org/10.1016/j.gexplo.2018.09.002
[60]. Hajihosseinlou, M., Maghsoudi, A., & Ghezelbash, R. (2024). Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches. Journal of Geochemical Exploration, 258, 107393.
[61]. Hoseinzade, Z., & Mokhtari, A. R. (2017). A comparison study on detection of key geochemical variables and factors through three different types of factor analysis. Journal of African Earth Sciences, 134, 557-563.
[62]. Saadati, H., Afzal, P., Torshizian, H., & Solgi, A. (2020). Geochemical exploration for lithium in NE Iran using the geochemical mapping prospectivity index, staged factor analysis, and a fractal model. Geochemistry: Exploration, Environment, Analysis, 20(4), 461-472.
[63]. Wu, R., Chen, J., Zhao, J., Chen, J., & Chen, S. (2020). Identifying geochemical anomalies associated with gold mineralization using factor analysis and spectrum–area multifractal model in Laowan District, Qinling-Dabie Metallogenic Belt, Central China. Minerals, 10(3), 229.
[64]. Meigoony, M. S., Afzal, P., Gholinejad, M., Yasrebi, A. B., & Sadeghi, B. (2014). Delineation of geochemical anomalies using factor analysis and multifractal modeling based on stream sediments data in Sarajeh 1: 100,000 sheet, Central Iran. Arabian Journal of Geosciences, 7, 5333-5343.
[65]. Yousefi, M., Kamkar-Rouhani, A., & Carranza, E. J. M. (2014). Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochemistry: Exploration, Environment, Analysis, 14(1), 45-58.
[66]. Aryafar, A., Yousefi, S., Khosravi, V., & Khorashadi, M. (2020). Using stepwise factor analysis (SFA) and geochemical mineralization probability index (GMPI) in order to intensify the geochemical anomalies associated with vein-type copper mineralization in Kardgan 1: 100000 sheet, East of Iran. Journal of Mining Engineering, 15(47), 1-13.
[67]. Afzal, P., Yusefi, M., Mirzaie, M., Ghadiri-Sufi, E., Ghasemzadeh, S., & Daneshvar Saein, L. (2019). Delineation of podiform-type chromite mineralization using geochemical mineralization prospectivity index and staged factor analysis in Balvard area (SE Iran). Journal of Mining and Environment, 10(3), 705-715.
[68]. Yousefi, M., & Carranza, E. J. M. (2015). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69-81.
[69]. Hoseinzade, Z., Zavarei, A., & Shirani, K. (2021). Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation. Natural Hazards, 109, 2489-2507.
[70]. Roshanravan, B., Aghajani, H., Yousefi, M., & Kreuzer, O. (2019). An improved prediction-area plot for prospectivity analysis of mineral deposits. Natural Resources Research, 28, 1089-1105.
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