[1] McCuaig, T.C., & Hronsky, J. M. A. (2014). The mineral system concept: The key to exploration targeting, Applied Earth Science IMM Transactions section B 18(2):153-175.
[2] Wyman, D.A., Cassidy, K., & Hollings, P. (2016). Orogenic gold and the mineral systems approach: Resolving fact, fiction and fantasy, Ore Geol. Rev. 78, 322-335.
[3] Groves, D.I., Santosh, M., & Zhang, L. (2020). A scale-integrated exploration model for orogenic gold deposits based on a mineral system approach, Geoscience Frontiers, 11(3), 719-738.
[4] Carranza, E. J. M., & Laborte, A. G. (2016). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of random forests algorithm. Ore Geol. Rev. 71, 777–787.
[5] Agterberg, F.P. (1992). Combining indicator patterns in weights of evidence modeling for resource evaluation. Nat. Resour. Res. 1, 39–50.
[6] Bonham-Carter, G.F. (1994). Geographic Information Systems for Geoscientists, Modelling with GIS. Pergamon, New York, 398 p.
[7] Abedi, M., & Norouzi, G.H. (2012). Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. J. Appl. Geophys. 83, 35–45.
[8] Parsa, M., & Maghsoudi, A. (2021). Assessing the effects of mineral systems-derived exploration targeting criteria for Random Forests-based predictive mapping of mineral prospectivity in Ahar-Arasbaran area, Iran. Ore Geol. Rev. 104399
[9] Chen, Y., & Wu, W. (2015). A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis. Ore Geol. Rev. 74, 26–38.
[10] Parsa, M., Maghsoudi, A., & Yousefi, M. (2017). An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets. Int. J. Appl. Earth Obs. 58, 157–167.
[11] Parsa, M., Maghsoudi, A., & Yousefi, M. (2017). A Receiver Operating Characteristics-Based Geochemical Data Fusion Technique for Targeting Undiscovered Mineral Deposits. Nat. Resour. Res. 27, 15-28.
[12] Porwal, A., Carranza, E.J.M., & Hale, M. (2006). A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Nat. Resourc. Res. 15, 1–14.
[13] Pazand, K., & Hezarkhani, A. (2015). Porphyry Cu potential area selection using the combine AHP-TOPSIS methods: a case study in Siahrud area (NW, Iran). Earth Sci. Inf. 8, 207–220.
[14] 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. Comput. Geosci. 79, 69–81.
[15] Yousefi, M., & Nykänen, V. (2016). Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral potential mapping. Journal of Geochemical Exploration 164, 94–106.
[16] Bahrami, Y., Hassani, H., & Maghsoudi, A. (2019). BWM-ARAS: A new hybrid MCDM method for Cu prospectivity mapping in the Abhar area, NW Iran. Spatial Statistics, 33, https://doi.org/10.1016/j.spasta.2019.100382
[17] Yousefi, M., & Carranza, E.J.M. (2016). Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values. J. Afr. Earth Sci. 128, 47–60.
[18] Bahrami, Y., Hassani, H., & Maghsoudi, A. (2022). Spatial modeling for mineral prospectivity using BWM and COPRAS as a new HMCDM method. Arab J Geosci 15, 394. https://doi.org/10.1007/s12517-022-09630-1
[19] Riahi, Sh., Bahroudi, A., Abedi, M., & Aslani, S. (2022).
A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: The case study of Chahargonbad area, SE of Iran, Geocarto International 38(1), 10.1080/10106049.2022.2159068
[20] Abedi, M., Norouzi, G.H., & Fathianpour, N. (2015). Mineral potential mapping in Central Iran using fuzzy ordered weighted averaging method, Geophysical Prospecting, 63, 461–477.
[21] Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis, International Journal of Applied Earth Observation and Geoinformation 8, 270–277.
[22] Cheng, Q., Agterberg, F.P., & Ballantyne, S.B. (1994). The separation of geochemical anomalies from background by fractal methods. J. Geochem. Explor. 51, 109–130.
[23] Yousefi, M., & Carranza, E.J.M. (2015). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Comput. Geosci. 74, 97–109.
[24] 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. Comput. Geosci. 83, 72–79.
[25] Alavi M. (1991). Sedimentary and structural characteristics of the Paleo-Tethys remnants in northeastern Iran. Geological Society of America Bulletin 103, 983-992.
[26] Berberian, F., Muir, I.D., Pankhurst, R.J. & Berberian, M. (1982). Late cretaceous and early miocene andean-type plutonic activity in northern Makran and central Iran.
Journal of the Geological Society 139, 605−614.
[27] Khan-Nazer, N.H., Emami, M.H., & Ghaforie, M. (1995). Geological Map of Chahargonbad. Geological Survey of Iran publication, Tehran, Iran (1: 100,000).
[28] Kianpourian, S., Farhamandian, M., Karimi, M., & Bahroudi, A. (2013). Preparation of mineral potential map for copper deposits using mixed neuroFuzzy model: a case study of 1:100,000 sheet of Chahargonbad in Kerman province. Journal of Earth Sciences, (Rocks and Minerals), 277-286
https://sid.ir/paper/31354/fa
[29] Riahi, Sh., Bahroudi, A., Abedi, M., & Aslani, S. (2022). Hybrid Outranking of Geospatial Data: Multi Attributive Ideal-Real Comparative Analysis and Combined Compromise Solution, Chemie der Erde - Geochemistry 82(6), 10.1016/j.chemer.
2022.125898
[30] Riahi, Sh., Bahroudi, A., Abedi, M., Aslani, S., & Elyasi, G. (2021). Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran. Geophysical Prospecting 69(5), 1116–1137. doi:10.1111/1365-2478.13092
[31] Riahi, Sh., Bahroudi, A., Abedi, M., Aslani, S., & Lentz, D.R.
(2021). Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data driven methods, Geophysical Prospecting 70(2), 421-437. https://doi.org/10.1111/1365-2478.13169
[32] Yager, R. R., & Filev, D. (1998). Operations for granular computing: mixing words and numbers, in Proceedings of the IEEE International Conference on Fuzzy Systems, 123–128, Anchorage, Alaska, USA.
[33] Malczewski, J. (1999). GIS and multicriteria decision analysis: John Wiley & Sons.
[34] Zadeh L.A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications 9, 149–184.
[35] Fuller R. (1996). OWA operators in decision making. In: Exploring the Limits of Support Systems, Vol. 3 (ed C. Carlsoon), pp. 85–104. TUCS General Publications, Turku Centre for Computer Science, Abo Akademi University, Turkey.
[36] Nadi, S., & Delavar, M.R. (2011). Multi-criteria, personalized route planning using quantifier-guided ordered weighted averaging operators. International Journal of Applied Earth Observation and Geoinformation 13, 322–335.
[37] Elyasi, Gh.R., Bahroudi, A., & Abedi, M. (2019). Risk-Based Analysis in Mineral Potential Mapping: Application of Quantifier-Guided Ordered Weighted Averaging Method. Natural Resources Research, 28, 931-951.
[38] Mohebi, A., Mirnejad, H., Lentz, D., Behzadi, M., Dolati, A., Kani, A., & Taghizadeh, H. (2015). Controls on porphyry Cu mineralization around Hanza Mountain, south-east of Iran: An analysis of structural evolution from remote sensing, geophysical, geochemical and geological data. Ore Geology Reviews, 69, 187-198.
[39] 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, 5, 103005.