Mineral Potential Modeling of Porphyry Copper Deposits using Continuously-Weighted Spatial Evidence Layers and Union Score Integration Method | ||
| Journal of Mining and Environment | ||
| مقاله 10، دوره 12، شماره 3، مهر 2021، صفحه 743-751 اصل مقاله (3.2 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2021.10668.2026 | ||
| نویسندگان | ||
| E. Bahri1؛ A. Alimoradi* 1؛ M. Yousefi2 | ||
| 1Department of Mining Engineering, Imam Khomeini International University, Ghazvin, Iran | ||
| 2Department of Mining Engineering, Malayer University, Malayer, Iran | ||
| چکیده | ||
| There are different exploration methods, each of which may introduce a number of promising exploration targets. However, due to the financial and time constraints, only a few of them are selected as the exploration priorities. Instead of the individual use of any exploration method, it is common to integrate the results of different methods in an interdependent framework in order to recognize the best targets for further exploration programs. In this work, the continuously-weighted evidence maps of proximity to intrusive contacts, faults density, and stream sediment geochemical anomalies of a set of porphyry copper deposits in the Jiroft region of the Kerman Province in Iran are first generated using the logistic functions. The weighted evidence maps are then integrated using the union score integration function in order to model the deposit type in the studied area. The weighting and integration approaches applied avoid the disadvantages of the traditional methods in terms of carrying the bias and error resulting from the weighting procedure. Evaluation of the ensuing prospectivity model generated demonstrate that the prediction rate of the model is acceptable, and the targets generated are reliable to follow up the exploration program in the studied area. | ||
| کلیدواژهها | ||
| Modeling؛ Integration؛ Exploration targets؛ Porphyry copper؛ Jiroft | ||
|
سایر فایل های مرتبط با مقاله
|
||
| مراجع | ||
|
[1]. Yousefi, M., Kreuzer, O.P., Nykänen, V., and Hronsky, J.M.A. (2019). Exploration information systems―a proposal for the future use of GIS in mineral exploration targeting. Geology Reviews 111, 103005.
[2]. Afzal, P., Yousefi, M., Mirzaei, M., Ghadiri-Sufi, E., Ghasemzadeh, S. and Daneshvar Saein, L. (2019). Delineation of podiform-type chromite mineralization using Geochemical Mineralization Prospectivity Index (GMPI) and staged factor analysis in Balvard area (southern Iran). Journal of Mining and Environment 10: 705-715.
[3]. Carranza, E.J.M. (2008). Geochemical Anomaly and Mineral Prospectivity Mapping in GIS. Handbook of Exploration and Environmental Geochemistry. Vol. 11, Elsevier, Amesterdam.
[4]. Yousefi, M. and Carranza, M.J.M. (2015). Fuzzification of Continuous-value spatial evidence for mineral prosprctivity mapping. Computers & Geosciences 74: 97-109.
[5]. Yousefi, M., Carranza, M.J.M. (2017). Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values. Journal of African Earth Sciences 128: 47-60.
[6]. Ghaeminejad, H., Abedi, M., Afzal, P., Zaynali, F., and Yousefi, M. (2020). A fractal-based outranking approach for mineral prospectivity analysis. Bollettino di Geofisica Teorica e Applicata. 61 (4): 555-588.
[7]. Yousefi, M., Kamkar-Rouhani, A., and Carranza, M.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.
[8]. Yousefi, M. and Khaloo Kakaei, R. (2004). Mineral Potential Modeling of Gold in Mahneshan Area using GIS. Mining Engineering Conference, Tarbiat Moddares University.
[9]. Saadati, H., Afzal, P., Torshian, H., and Solgi, A. (2020). Geochemical exploration for Li using Geochemical Mapping Prospectivity Index (GMPI), fractal and Stage Factor Analysis (SFA) in NE Iran. Geochemistry: Exploration, Environment, Analysis 20: 461-472.
[10]. Kianpoorian, S., Farahmandian, M., Karimi, M., and Bahroodi, A. (2014). Mineral Potential Modeling of copper using Neuro-Fuzzy Model in Chargonbad, Kerman. Earth Science Journal, 94: GSI.
[11]. Berberian, F., Muir, I.D., Pankhurst, R.J., and Berberian, M. (1982). Late cretaceous and early miocene andean-type plutonic activity in northern Makran and central Iran. J. Geol. Soc. Lond. 139, 605e614.
[12]. Badrzadeh, Z. and Aghazadeh, M. (2014). Geochemistry and Structural Geology of Intrusive Masses of South-Western Part of Jiroft. Geochemistry Journal, 2, Payame Noor University
[13]. Roberts, R.G., Sheahan, P., and Cherry, M.E. (1998). Ore Deposit Models. Geoscience Canada Reprint Series 3, Geological Association of Canada, Newfoundland.
[14]. Berger, B. R. and Drew, L.J. (2002). Mineral–deposit models: new developments in: A.G. Fabbri, Gaal, G., Mccammon, R.B. (Eds.), Deposit and Geoenviromental models for Rsource Exploration and Enviromental Security. NATO Science Series 2, 80: 121-134.
[15]. Arribas, A.J. (1995). Contemporaneous formation of adjacentporphyry and epithermal Cu-Au deposits over 300 ka innorthern Luzon, Philippines. Geology, 23: 337–340.
[16]. Singer, D.A., Berger, V.I., and Moring, B.C. (2005). Porphyry copper deposits of the world: database, map, grade, and tonnage models. U.S. Geological Survey. Open-File Report: 1005–1060.
[17]. Hezarkhani, A. (2006). Mineralogy and fluid inclusion investigations in the Reagan Porphyry System, Iran, the path to an uneconomic porphyry copper deposit. Journal of Asian Earth Sciences 27: 598–612.
[18]. Guillou-Frottier, L., and Burov, E. (2003). The development and fracturing of plutonic apexes: Implications for porphyry ore deposits. Earth and Planetary Science Letters 214: 341–356.
[19]. Qu, X., Hou, Z., Zaw, K., and Youguo, L. (2007). Characteristics and genesis of Gangdese porphyry copper deposits in the southern Tibetan Plateau: Preliminary geochemical and geochronological results. Ore Geology Reviews 31: 205–223.
[20]. Ghasemi, A. and Talbot, C.J. (2006). A new tectonic scenario for the Sanandaj–Sirjan Zone (Iran). Journal of Asian Earth Sciences 26: 683–693.
[21]. Meshkani, S.A., Mehrabi, B., Yaghubpur, A. and Sadeghi, M. (2013). Recognition of the regional lineaments of Iran: using geospatial data and the implications for exploration of metallic ore deposits. Ore Geology Reviews 55: 48–63.
[22]. Zare Chahooki, M.A. (2010). Moltivariate Analysis in SPSS. University of Tehran.
[23]. Carranza, E.J.M., Woladi, T., and Chikambwe, E.M. (2005). Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmaties, Lundazi District, Zambia. Natural Resource Reserves 14: 47-63.
[24]. Bonham-Carter, G.F., Agterberg, F.P., and Wright, D.F. (1989). Weight of evidence modeling: a new approach to mapping mineral potential In: Agterberg, F.P., Bonham-Carter, G.F. (Eds.), Statistical Applications in the Earth Science. Geological Survey of Canada 89: 171-183.
[25]. Nezhad, S.G., Mokhtari, A.R. and Rodsari, P.R. (2017). The true sample catchment basin approach in the analysis of stream sediment geochemical data. Ore Geology Reviews. 83: 127–134.
[26]. Zhang, N., Zhou, K., and Du, X. (2017). Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu- Tousuquan island arc, Xinjiang, NW China. Journal of African Earth Sciences 128: 84–96.
[27]. Du, X., Zhou, K., Cui, Y., Wang, J., Zhang, N. and Sun, W. (2016). Application of fuzzy Analytical Hierarchy Process (AHP) and Prediction-Area (PA) plot for mineral prospectivity mapping: A case study from the Dananhu metallogenic belt, Xinjiang, NW China. Arabian Journal of Geosciences 9: 298.
[28]. Roshanravan, B., Aghajani, H., Yousefi, M. and Kreuzer, O. (2018). An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits. Natural Resources Research. | ||
|
آمار تعداد مشاهده مقاله: 679 تعداد دریافت فایل اصل مقاله: 2,203 |
||
