حسینزاده، م.ر.، مغفوری، س.، قربانی، م.، موید، م.، 1395، انواع سامانه رگه- رگچه مرتبط با کانهزایی و مطالعات میانبارهای سیال در کانسار مس- مولیبدن پورفیری سوناجیل، پهنه ماگمایی ارسباران، فصلنامه علمی علوم زمین، 26(101)، ص. 219-230. https://doi.org/10.22071/gsj.2016.41069.
شادمان، م.، تخمچی، ب.،1393، مقایسه روشهای خوشهبندی در تهیه نقشه پتانسیل معدنی در بیهنجاری باریکا، آذربایجان غربی، نشریه علمی-پژوهشی علوم زمین، 94(24)، ص. 67 تا72. .DOI:10.22071/gsj.2015.43118
کیخای حسینپور، م.، کوهساری، ا.، حسین مرشدی، ا.، و پروال، آ.، 1400، مدلسازی پتانسیل کانیسازی مس و طلای پورفیری با به کارگیری روش یادگیری نیمه نظارتی در پهنه اکتشافی دهسلم، شرق ایران، نشریه علمی پژوهشی زمین شناسی اقتصادی، 13(1)،ص 193 تا 213. OI:10.22067/econg.v13i1.81382 .
Abedi, M., Norouzi, G.H., and Fathianpour, N., 2013. Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping. International Journal of Applied Earth Observation and Geoinformation, 21, pp.556-567.https://doi.org/10.1016/j.jag.2012.07.012.
Aghazadeh, M., Hou, Z., Badrzadeh, Z., and 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, pp.385-406. https://doi.org/10.1016/j.oregeorev.2015.03.003.
Alesheikh, A.A., Soltani, M.J., Nouri, N., and Khalilzadeh, M., 2008. Land assessment for flood spreading site selection using geospatial information system. International Journal of Environmental Science & Technology, 5(4), pp.455-462. https://doi.org/10.1007/BF03326041.
Aryafar, A., and Roshanravan, B., 2020. Improved index overlay mineral potential modeling in brown-and green-fields exploration using geochemical, geological and remote sensing data. Earth Science Informatics, 13, pp.1275-1291. https://doi.org/10.1007/s12145-020-00509-x.
Barak Bahroudi, A., and Jozani Kohen, G., 2018. Integration of copper information layers in the Nisian area with the help of fuzzy inference system (FIS). Scientific-Research Journal of Mining Engineering, 13(38), 97-112.
Beane, R.E., 1995. Hydrothermal fluids and hydrothermal alteration in porphyry copper deposits. Porphyry copper deposits of the American Cordillera, pp.83-93.
Bensaid, A.M., Hall, L.O., Bezdek, J.C., Clarke, L.P., Silbiger, M.L., Arrington, J.A., and Murtagh, R.F., 1996. Validity-guided (re) clustering with applications to image segmentation. IEEE Transactions on fuzzy systems, 4(2), pp.112-123. https://doi.org/10.1109/91.493905.
Beus, A.A., and Grigorian, S.V., 1962.Geochemical exploration methods for mineral deposits, Trans. by R.T. Schneider, ed. by A.A. Levinson, Illiois: Applied publishing.
Bezdek, J.C., and Bezdek, J.C., 1981. Objective function clustering. Pattern recognition with fuzzy objective function algorithms, pp.43-93. https://doi.org/10.1007/978-1-4757-0450-1_3.
Bonham-Carter, G.F., Agterberg, F.P., and Wright, D.F., 1988. Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetric Engineering and Remote Sensing, 54(11), pp.1585-1592. https://doi.org/10.1029/SC010p0015.
Carranza, E J M., 2009."Geochemical anomaly and mineral prospectivity mapping in GIS. Handbook of Exploration and Environmental Geochemistry, Vol. 11, M. Hale (Series Editor)."
Carranza, E.J.M., 2008. Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.
Cheng, H., Zheng, Y., Wu, S., Lin, Y., Gao, F., Lin, D., and Chen, L., 2023. GIS-based mineral prospectivity mapping using machine learning methods: a case study from Zhuonuo ore district, Tibet. Ore Geology Reviews, 161, 105627. https://doi.org/10.1016/j.oregeorev.2023.105627.
Duda, R. A., Hart, P. E., and Stork, D.G., 2012. Pattern Classification. John Wiley and Sons.
Faruwa, A. R., Ba, J., Qian, W., Markus, U. I., and Bachri, I., 2025. Implementation of machine learning predictive models for targeting gold prospectivity mapping in part of the Ilesha schist belt, southwestern Nigeria. Journal of African Earth Sciences, 105543. https://doi.org/10.1016/j.jafrearsci.2025.105543.
Ghavami-Riabi, R., Seyedrahimi-Niaraq, M.M., Khalokakaie, R., and Hazareh, M.R., 2010. U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits. Journal of Geochemical exploration, 104(1-2), pp.27-33. https://doi.org/10.1016/j.gexplo.2009.10.002.
Gustafson, D. E., and Kessel, W. C., 1979. Fuzzy clustering with a fuzzy covariance matrix. In 1978 IEEE conference on decision and control including the 17th symposium on adaptive processes (pp. 761-766). IEEE. https://doi.org/10.1109/CDC.1978.268028.
Hosseinzadeh, M.R., Maghfouri, S., Ghorbani, M. and Moayyed, M., 2017. Different types of vein- veinlets related to mineralization and fluid inclusion studies in the Sonajil porphyry Cu- Mo deposit, Arasbaran magmatic zone. Scientific Quarterly Journal of Geosciences 101,2219-230 (in Persian). https://doi.org/10.22071/gsj.2016.41069.
Jahangiri, M., Ghavami Riabi, S.R., and Tokhmechi, B., 2018. Estimation of geochemical elements using a hybrid neural network-Gustafson-Kessel algorithm. Journal of Mining and Environment, 9(2), pp.499-511. https://doi.org/10.22044/jme.2017.5513.1363.
Keykhay-Hosseinpoor, M., Kouhsari, A., Morshedy, A H., and Porwal, A. 2021. Porphyry Cu-Au prospectivity modelling using semi-supervised learning algorithm in Dehsalm district, eastern iran. Journal of Economic Geology,13(1),193-21378. https://doi.org/10.22067/econg.v13i1.81382 (In Persian).
Lagat, J., 2009. Hydrothermal alteration mineralogy in geothermal fields with case examples from Olkaria domes geothermal field, Kenya. Dipresentasikan dalam short course IV on exploration for geothermal resources.
Lesot, M.J., and Kruse, R., 2008. Gustafson-Kessel-like clustering algorithm based on typicality degrees. In Uncertainty and Intelligent Information Systems (pp. 117-130). https://doi.org/10.1142/9789812792358_0009.
Li, Q., Chen, G., and Wang, D., 2025. Mineral Prospectivity Mapping Using Semi-supervised Machine Learning. Mathematical Geosciences, 57(2), 275-305. https://doi.org/10.1007/s11004-024-10161-6.
Liu, H., Harris, J., Sherlock, R., Behnia, P., Grunsky, E., Naghizadeh, M., and Hill, G., 2023. Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada. Journal of Geochemical Exploration, 253, 107279. https://doi.org/10.1016/j.gexplo.2023.107279.
Liu, Y., Cheng, Q., and Zhou, K., 2019. New insights into element distribution patterns in geochemistry: A perspective from fractal density. Natural Resources Research, 28, pp.5-29. https://doi.org/10.1007/s11053-018-9374-7.
Mahdiyanfar, H., and 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), pp.1019-1035. https://doi.org/10.22044/jme.2023.13227.2424.
Mohammadzadeh, M., and Nasseri, A., 2018. Geochemical modeling of orogenic gold deposit using PCANN hybrid method in the Alut, Kurdistan province, Iran. Journal of African Earth Sciences, 139, pp.173-183. https://doi.org/10.1016/j.jafrearsci.2017.11.038.
Moshefi, P., Hosseinzadeh, M.R., Moayyed, M., and Lentz, D.R., 2018. Comparative study of mineral chemistry of four biotite types as geochemical indicators of mineralized and barren intrusions in the Sungun Porphyry Cu-Mo deposit, northwestern Iran. Ore Geology Reviews, 97, pp.1-20. https://doi.org/10.1016/j.oregeorev.2018.05.003.
Sadeghi, M., Casey, P., Carranza, E. J. M., and Lynch, E. P., 2024. Principal components analysis and K-means clustering of till geochemical data: Mapping and targeting of prospective areas for lithium exploration in Västernorrland Region, Sweden. Ore Geology Reviews, 167, 106002. https://doi.org/10.1016/j.oregeorev.2024.106002.
Salehi, T., and Tangestani, M.H., 2020. Per-pixel analysis of ASTER data for porphyry copper hydrothermal alteration mapping: a case study of NE Isfahan, Iran. Remote Sensing Applications: Society and Environment, 20, p.100377. https://doi.org/10.1016/j.rsase.2020.100377.
Serir, L., Ramasso, E., and Zerhouni, N., 2012. Evidential evolving Gustafson–Kessel algorithm for online data streams partitioning using belief function theory. International journal of approximate reasoning, 53(5), pp.747-768. https://doi.org/10.1016/j.ijar.2012.01.009.
Shademan, M., and Tokhmechi, B., 2015.Comparison of Clustering Methods in Mineral Potential Mapping of Barika Anomaly, West Azerbaijan. Journal of Geological Survey of Iran. 24(94),67-72. https://doi.org/10.22071/gsj.2015.43118. (In Persian).
Shirmard, B., Bahroudi, A., and adeli, A., 2015. Fuzzy hierarchical analysis method in spatial information system in order to determine optimal drilling points in Nisian porphyry copper deposit. Scientific-Research Quarterly of Geographical Information "Sephehr", 24(93), 91-100. https://doi.org/10.22131/sepehr.2015.14010.
Sun, G., Zeng, Q., and Zhou, J.X., 2022. Machine learning coupled with mineral geochemistry reveals the origin of ore deposits. Ore Geology Reviews, 142, p.104753. https://doi.org/10.1016/j.oregeorev.2022.104753.
Wang, Z., Zhou, C., and Qin, H., 2020. Detection of hydrothermal alteration zones using ASTER data in Nimu porphyry copper deposit, south Tibet, China. Advances in Space Research, 65(7), pp.1818-1830. https://doi.org/10.1016/j.asr.2020.01.008.
Wang, Zh., Cheng, Q., Xu, D., and Dong, Y., 2008. Fractal Modeling of Sphalerite Banding in Jinding Pb-Zn Deposit, Yunnan, Southwestern China, Journal of China University of Geosciences, Volume 19, Issue 1, 77-84, 1002-0705. https://doi.org/10.1016/S1002-0705(08)60027-8.
Xie, X.L., and Beni, G., 1991. August. A new fuzzy clustering validity criterion and its application to color image segmentation. In Proceedings of the 1991 IEEE International Symposium on Intelligent Control (pp. 463-468). IEEE. https://doi.org/10.1109/ISIC.1991.187401.
Yeomans, C. M., Shail, R. K., Grebby, S., Nykänen, V., Middleton, M., and Lusty, P. A. 2020. A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence. Geoscience Frontiers, 11(6), 2067-2081. https://doi.org/10.1016/j.gsf.2020.05.016.
Zhao, Z.F., Zhou, J.X., Lu, Y.X., Chen, Q., Cao, X.M., He, X.H., Fu, X.H., Zeng, S.H., and Feng, W.J., 2021. Mapping alteration minerals in the Pulang porphyry copper ore district, SW China, using ASTER and WorldView-3 data: Implications for exploration targeting. Ore Geology Reviews, 134, p.104171. https://doi.org/10.1016/j.oregeorev.2021.104171.
Zuo, R., 2017. Machine learning of mineralization-related geochemical anomalies: A review of potential methods. Natural Resources Research, 26, pp.457-464. https://doi.org/10.1007/s11053-017-9345-4.
Zuo, R., Kreuzer, O.P., Wang, J., Xiong, Y., Zhang, Z., and Wang, Z., 2021. Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions. Natural Resources Research, 30, pp.3059-3079. https://doi.org/10.1007/s11053-021-09871-z.