- حسنیپاک، علیاصغر. (۱۳۹۴). اصول اکتشافات ژئوشیمیایی. تهران: انتشارات دانشگاه تهران.
- رجبی, عادلی نیک. (2024). تاثیر رویکرد سیاستگذاریهای معدنی دولت در جبران پیامدهای زیستمحیطی در ایران. پژوهشنامه بازرگانی, 28(109), 1-33.
https://doi.org/10.22034/ijts.2024.2005685.3869
- سازمان توسعه و نوسازی معادن و صنایع معدنی ایران,(1398) “گزارش نهایی اکتشاف منابع معدنی پلیمتال در پهنه بشاگرد، استان هرمزگان,” شرکت مهندسین مشاور زمین آب پی, مهرماه.
- عادلی نیک, رجبی. (2023). تاثیر بهرهبرداری از معادن بر سطح استاندارد زندگی مردم ساکن مناطق میزبان معدن، مطالعه موردی ایران. پژوهشنامه بازرگانی, 27(106), 35-66. .
10.22034/ijts.2023.1982683.3760
- نقشه 1/250000 فنوج، سازمان زمینشناسی و اکتشافات معدنی کشور، تهران، ایران.
- Adeli Nik, R., & Rajabi, R. (2023). Tāsire bahrebardāri az ma’āden bar satahe standarde zendegiye mardome sākene manāteqe mizbāne ma’dan: Motāle’eye morediye Irān. Pajouheshnamehye Bazargani, 27(106), 35-66. 10.22034/ijts.2023.1982683.3760 [In Persian]
- Doherty, M. E., Arndt, K., Chang, Z., Kelley, K., & Lavin, O. (2023). Stream sediment geochemistry in mineral exploration: a review of fine-fraction, clay-fraction, bulk leach gold, heavy mineral concentrate and indicator mineral chemistry. Geochemistry: Exploration, Environment, Analysis, 23(4), geochem2022-039. https://doi.org/10.1144/geochem2022-039
- Geological Survey of Iran. (n.d.). Fanouj 1:250000 geological map. Tehran, Iran. [In Persian]
- HasaniPak, A. A. (2015). Osule ekteshefate geochemiyayi [Principles of geochemical exploration]. Tehran: University of Tehran Press. [In Persian]
- Iranian Mines and Mining Industries Development and Renovation Organization (IMIDRO). (2019). Final exploration report of polymetal mineral resources in the Bashagard zone, Hormozgan Province. Zamin Ab Pey Consulting Engineers. [In Persian]
- Jozanikohan, G., Nosrati Abarghooei, M., & Sedighi, H. (2021). Geochemical study of rare earth elements content in Tabas Coal Ash, Parvadeh Coal Mine. Journal of Mining and Environment, 12(4), 1143-1153.
- Lachaud, A., Adam, M., & Mišković, I. (2023). Comparative study of random forest and support vector machine algorithms in mineral prospectivity mapping with limited training data. Minerals, 13(8), 1073. https://doi.org/10.3390/min13081073
- McKay, G., & Harris, J. R. (2016). Comparison of the data-driven random forests model and a knowledge-driven method for mineral prospectivity mapping: A case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada. Natural Resources Research, 25(2), 125-143. https://doi.org/10.1007/s11053-015-9274-z
- Meloni, F., Gozzi, C., Cabassi, J., Nisi, B., Rappuoli, D., & Vaselli, O. (2026). Integrating Compositional Data Analysis (CoDA) and Random Forest for lithology-specific geochemical baseline determination. Science of the Total Environment, 1011, 181169. https://doi.org/10.1016/j.scitotenv.2025.181169
- Mohammadpour, M., Bahroudi, A., Abedi, M., Rahimipour, G., Jozanikohan, G., & Khalifani, F. M. (2019). Geochemical distribution mapping by combining number-size multifractal model and multiple indicator kriging. Journal of Geochemical Exploration, 200, 13-26. https://doi.org/10.1016/j.gexplo.2019.01.018
- Rajabi, R., & Adeli Nik, R. (2024). Tāsire roykardhāye siāsatgozāriye ma’daniye dowlat dar jobrāne payāmadhāye zistmohiti dar Irān. Pajouheshnamehye Bazargani, 28(109), 1-33. https://doi.org/10.22034/ijts.2024.2005685.3869 [In Persian]
- Reimann, C., Filzmoser, P., Garrett, R., & Dutter, R. (2011). Statistical data analysis explained: applied environmental statistics with R. John Wiley & Sons.
- Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. J. O. G. R. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore geology reviews, 71, 804-818. https://doi.org/10.1016/j.oregeorev.2015.01.001
- Schnitzler, N., Ross, P. S., & Gloaguen, E. (2019). Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data. Journal of Geochemical Exploration, 205, 106344. https://doi.org/10.1016/j.gexplo.2019.106344
- Trott, M., Leybourne, M., Hall, L., & Layton-Matthews, D. (2022). Random forest rock type classification with integration of geochemical and photographic data. Applied Computing and Geosciences, 15, 100090. https://doi.org/10.1016/j.acags.2022.100090
- ZhangZhou, J., He, C., Sun, J., Zhao, J., Lyu, Y., Wang, S., … & Agarwal, A. (2024). Geochemistry π: automated machine learning python framework for tabular data. Geochemistry, Geophysics, Geosystems, 25(1), e2023GC011324. https://doi.org/10.1029/2023GC011324
- Zheng, C., Yuan, F., Luo, X., Li, X., Liu, P., Wen, M., … & Albanese, S. (2023). Mineral prospectivity mapping based on Support vector machine and Random Forest algorithm-A case study from Ashele copper-zinc deposit, Xinjiang, NW China. Ore Geology Reviews, 159, 105567. https://doi.org/10.1016/j.oregeorev.2023.105567
- Zhong, R., Deng, Y., Li, W., Danyushevsky, L. V., Cracknell, M. J., Belousov, I., … & Li, L. (2021). Revealing the multi-stage ore-forming history of a mineral deposit using pyrite geochemistry and machine learning-based data interpretation. Ore Geology Reviews, 133, 104079. https://doi.org/10.1016/j.oregeorev.2021.104079
- Zhou, S., Cheng, Z., Wang, J., Li, N., & Jiang, G. (2025). Uncover implicit associations among geochemical elements using machine learning. Ore Geology Reviews, 179, 106506. https://doi.org/10.1016/j.oregeorev.2025.106506
- Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019). Deep learning and its application in geochemical mapping. Earth-science reviews, 192, 1-14. https://doi.org/10.1016/j.earscirev.2019.02.023
