Combination of Geochemical and Structural Data to Determine Exploration Target of Copper Hydrothermal Deposits in Feizabad District | ||
| Journal of Mining and Environment | ||
| مقاله 16، دوره 15، شماره 3، مرداد 2024، صفحه 1089-1101 اصل مقاله (13.9 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2024.13698.2562 | ||
| نویسندگان | ||
| Mobin Saremi1؛ Saeed Yousefi* 2؛ Mahyar Yousefi1 | ||
| 1Department of Mining Engineering, Faculty of Engineering, Malayer University, Malayer, Iran | ||
| 2Department of Mining, Faculty of Engineering, University of Birjand, Birjand, Iran | ||
| چکیده | ||
| The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time shortages. In this regard, geochemical anomaly maps constitute one of the most important evidential layers for MPM. In this research work, to produce an efficient geochemical evidential layer, the Staged Factor Analysis (SFA) method and Geochemical Mineralization Probability Index (GMPI) were performed on a dataset of 657 stream sediment samples. In addition to the mentioned maps, a layer of proximity to faults was used to efficiently identify the intended targets of copper hydrothermal deposits. The layers were then weighted and combined using logistic functions and the geometric average method. Based on the obtained results, the promising areas were found in three parts including western, central, and northern areas, which correspond to the faulted units of andesite, tuff, granite, and granodiorite intrusive masses. Finally, in order to evaluate the generated model, the prediction-area (P-A) plot was used, which shows the relative success of the generated map in specifying the desired exploration targets. The P-A plot showed that this model has a prediction rate of 64%. It seems that the proposed method by considering multi-element geochemical signatures and combination by another exploratory layer target the promising areas, those that are simultaneously present with other exploration evidence. | ||
| کلیدواژهها | ||
| Stage Factor Analysis؛ Geochemical Mineralization Probability Index؛ Geometric average؛ Mineral Prospectivity Mapping؛ Feizabad | ||
| مراجع | ||
|
[1]. Yousefi, M., Kreuzer, O. P., Nykänen, V., & Hronsky, J. M. (2019). Exploration information systems–A proposal for the future use of GIS in mineral exploration targeting. Ore Geology Reviews, 111, 103005.
[2]. Ghezelbash, R., Maghsoudi, A., Shamekhi, M., Pradhan, B., & Daviran, M. (2023). Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity. Neural Computing and Applications, 35(1), 719-733.
[3]. 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.
[4]. Bai, H., Cao, Y., Zhang, H., Wang, W., Jiang, C., & Yang, Y. (2022). Applying Data-Driven-Based Logistic Function and Prediction-Area Plot to Map Mineral Prospectivity in the Qinling Orogenic Belt, Central China. Minerals, 12(10), 1287.
[5]. 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.
[6]. 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.
[7]. Yousefi, M., Yousefi, S., & Kamkar Rouhani, A. G. (2023). Recognition coefficient of spatial geological features, an approach to facilitate criteria weighting for mineral exploration targeting. International Journal of Mining and Geo-Engineering.
[8]. 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.
[9]. 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.
[10]. 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.
[11]. Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.
[12]. Coolbaugh, M. F., Raines, G. L., & Zehner, R. E. (2007). Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources. Natural Resources Research, 16, 199-207.
[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]. 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.
[15]. Bahrami, Y., Hasani, H., & Maghsoudi, A. (2021). Application of AHP-TOPSIS method to model copper mineral potencial in the Abhar 1: 100000 geological map, NW Iran. Researches in Earth Sciences, 12(1), 41-57.
[16]. 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.
[17]. 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.
[18]. Ghadiyanloo, M., Alimoradi, A., & Yousefi, M. (2022). Recognizing Porphyry Copper Mineralization Targets in Chahar-Gonbad Area of Kerman Province Using Extreme Learning Intelligent Method. Journal of Mineral Resources Engineering, 7(1), 39-61.
[19]. Hajihosseinlou, M., Maghsoudi, A., & Ghezelbash, R. (2023). A Novel Scheme for Mapping of MVT-Type Pb–Zn Prospectivity: LightGBM, a Highly Efficient Gradient Boosting Decision Tree Machine Learning Algorithm. Natural Resources Research, 1-22.
[20]. 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.
[21]. Ghasemzadeh, S., Maghsoudi, A., Yousefi, M., & Mihalasky, M. J. (2022). Recognition and incorporation of mineralization-efficient fault systems to produce a strengthened anisotropic geochemical singularity. Journal of Geochemical Exploration, 235, 106967.
[22]. Chen, J., Yousefi, M., Zhao, Y., Zhang, C., Zhang, S., Mao, Z., ... & Han, R. (2019). Modelling ore-forming processes through a cosine similarity measure: Improved targeting of porphyry copper deposits in the Manzhouli belt, China. Ore Geology Reviews, 107, 108-118.
[23]. 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.
[24]. Cheng, Q., Agterberg, F. P., & Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical exploration, 51(2), 109-130.
[25]. Carranza, E. J. M., & Hale, M. (1997). A catchment basin approach to the analysis of reconnaissance geochemical-geological data from Albay Province, Philippines. Journal of Geochemical Exploration, 60(2), 157-171.
[26]. Zuo, R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration, 111(1-2), 13-22.
[27]. Mokhtari, A. R., & Nezhad, S. G. (2015). A modified equation for the downstream dilution of stream sediment anomalies. Journal of Geochemical Exploration, 159, 185-193.
[28]. Shahrestani, S., & Mokhtari, A. R. (2017). Improved detection of anomalous catchment basins by incorporating drainage density in dilution correction of geochemical residuals. Geochemistry: Exploration, Environment, Analysis, 17(3), 194-203.
[29]. 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.
[30]. Seyedrahimi-Niaraq, M., & Mahdiyanfar, H. (2021). Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization. Geochemistry, 81(4), 125830.
[31]. 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.
[32]. Zuo, R., & Wang, J. (2016). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration, 164, 33-41.
[33]. 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.
[34]. 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.
[35]. Shamseddin Meigooni, M., Lotfi, M., Afzal, P., Nezafati, N., & Razi, M. K. (2021). Application of multivariate geostatistical simulation and fractal analysis for detection of rare-earth element geochemical anomalies in the Esfordi phosphate mine, Central Iran. Geochemistry: Exploration, Environment, Analysis, 21(2), geochem2020-035.
[36]. Hosseini, S. A., Khah, N. K. F., Kianoush, P., Afzal, P., Ebrahimabadi, A., & Shirinabadi, R. (2023). Integration of fractal modeling and correspondence analysis reconnaissance for geochemically high-potential promising areas, NE Iran. Results in Geochemistry, 11, 100026.
[37]. Mahdiyanfar, H., & Seyedrahimi-Niaraq, M. (2023). Integration of Fractal and Multivariate Principal Component Models for Separating Pb-Zn Mineral Contaminated Areas. Journal of Mining and Environment, 14(3), 1019-1035.
[38]. Mahdiyanfar, H., & Seyedrahimi-Niaraq, M. (2022). Improvement of geochemical prospectivity mapping using power spectrum–area fractal modelling of the multi-element mineralization factor (SAF-MF). Geochemistry: Exploration, Environment, Analysis, 22(4), geochem2022-015.
[39]. Geranian, H. (2021). Application of multivariate transformation methods in geochemical data analysis of Hemych exploration area, South Khorasan Province. Journal of Analytical and Numerical Methods in Mining Engineering, 11(27), 1-18.
[40] Saremi, M., Yousefi, M., & Yousefi, S. (2023). Separation of geochemical anomalies related to hydrothermal copper mineralization using staged factor analysis in Feyzabad geological map. Journal of Analytical and Numerical Methods in Mining Engineering, in press, 10.22034/ANM.2023.19986.1593.
[41]. Ziaii, M., Pouyan, A. A., & Ziaei, M. (2009). Neuro-fuzzy modelling in mining geochemistry: identification of geochemical anomalies. Journal of Geochemical Exploration, 100(1), 25-36.
[42]. Pirdadeh Beyranvand, D., Arian, M. A., Farhadinejad, T., & Ashja Ardalan, A. (2021). Identification of Geochemical Distribution of REEs Using Factor Analysis and Concentration-Number (CN) Fractal Modeling in Granitoids, South of Varcheh 1: 100000 Sheet, Central Iran. Iranian Journal of Earth Sciences, 13(4), 288-289.
[43]. 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.
[44]. Reimann, C., Filzmoser, P., & Garrett, R. G. (2002). Factor analysis applied to regional geochemical data: problems and possibilities. Applied geochemistry, 17(3), 185-206.
[45]. 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.
[46]. 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.
[47]. A. Behroozi., "Geological map of Iran 1: 100,000 series, Feizabad. Geological Survey of Iran, Tehran (In Persian)," ed. 1987.
[48]. Saadat, S., & Ghoorchi, M. (2009). Primary analysis for enhancing the iron oxide and alteration minerals, using ETM+ data: a case study of Kuh-e-Zar gold deposit, NE Iran.
[49]. 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.
[50]. 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.
[51]. Yousefi, M., Kamkar-Rouhani, A., & Alipoor, M. (2014). Increasing the Exploration Success and Intensify of Stream Sediment Geochemical Halos Using Recognizing and Omitting the Non-Predictive Factors, Case Studies: Fluorite and Copper Mineralization. Scientific Quarterly Journal of Geosciences, 24(93), 85-92.
[52]. Moghadam, H. S., Li, X. H., Ling, X. X., Santos, J. F., Stern, R. J., Li, Q. L., & Ghorbani, G. (2015). Eocene Kashmar granitoids (NE Iran): Petrogenetic constraints from U–Pb zircon geochronology and isotope geochemistry. Lithos, 216, 118-135.
[53]. 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. | ||
|
آمار تعداد مشاهده مقاله: 905 تعداد دریافت فایل اصل مقاله: 722 |
||