Mining of Mineral Deposits

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Machine learning-assisted hyperspectral targeting of orogenic and placer gold deposits at Wadi Umm Eish El-Zarqa, Egypt

Sobhi M. Ghoneim1,2, Changcheng Wang1, Hala F. Ali1,2, Ibrahim I. Yahaya1, Mohamed Faisal3

1Department of Surveying and Remote Sensing, School of Geosciences and Info‑Physics, Central South University, Changsha, China

2Department of Mineral Resources, National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

3Department of Geology, Faculty of Science, Suez Canal University, El-Ismailia, Egypt


Min. miner. depos. 2026, 20(3): 10-22


https://doi.org/10.33271/mining20.03.010

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      ABSTRACT

      Purpose. This study develops a reproducible, precision-first framework for targeting gold mineralization in arid environments, addressing both bedrock and placer deposits at the Wadi Umm Eish El-Zarqa area in Egypt, the first such investigation in this region.

      Methods. The methodology combines machine learning applied to hyperspectral EnMAP data and spatial geomorphometric analysis of ALOS-PALSAR DEM. Random Forest and Support Vector Machine with Radial Basis Function models were trained on pixel spectra from three known gold mining sites. A spectral probability tolerance of 0.05 was implemented to expand the training dataset. Drainage networks were extracted from DEM data to link upstream bedrock sources to potential downstream placer deposits. ASTER-derived alteration maps were generated for comparative assessment.

      Findings. Ground-truth validation of the delineated mineralization targets showed SVM-RBF achieved slightly higher overall accuracy (91.8 vs 89.9%), RF delivered perfect precision in identifying mineralized targets, and crucially delineated 37% more high-confidence (> 80% probability) potentially mineralized targets than SVM-RBF. Drainage analysis identified 3rd to 5th-order streams as optimal zones for placer deposition. Fieldwork confirmed active artisanal excavations, with stream sediments reportedly yielding 25-30 grams per truckload (~85 tons).

      Originality. This study addresses a clear gap in hyperspectral investigations for mineral exploration in the Eastern Desert of Egypt. It is the first study to integrate machine learning with EnMAP hyperspectral data to target placer gold deposits in this region, establishing a precision-focused exploration approach.

      Practical implications. The framework provides a transferable methodology for mineral exploration in analogous arid terrains worldwide, reducing financial risk and enabling efficient allocation of follow-up resources.

      Keywords: EnMAP hyperspectral data; machine learning; random forest; support vector machine; placer gold; Umm Eish El-Zarqa


      REFERENCES

  1. Sadek, M.F., Ramadan, T.M., Abu El Leil, I., & Salem, S.M. (2006). Using remote sensing technique in lithological discrimination and detection of gold-bearing alteration zones at Wadi Defeit area, Southeastern Desert, Egypt. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, 6366, 63660H. https://doi.org/10.1117/12.685594
  2. Ghrefat, H., Kahal, A.Y., Abdelrahman, K., Alfaifi, H.J., & Qaysi, S.I. (2021). Utilization of multispectral Landsat-8 remote sensing data for lithological mapping of southwestern Saudi Arabia. Journal of King Saud University – Science, 33(4), 101414. https://doi.org/10.1016/j.jksus.2021.101414
  3. Ghoneim, S.M., Hamimi, Z., Abdelrahman, K., Khalifa, M.A., Shabban, M., & Abdelmaksoud, A.S. (2024). Machine learning and remote sensing-based lithological mapping of the Duwi Shear-Belt area, Central Eastern Desert, Egypt. Scientific Reports, 14(1), 17010. https://doi.org/10.1038/s41598-024-66199-3
  4. Eldosouky, A.M., Sehsah, H., Elkhateeb, S.O., & Pour, A.B. (2020). Integrating aeromagnetic data and Landsat-8 imagery for detection of post-accretionary shear zones controlling hydrothermal alterations: The Allaqi-Heiani Suture zone, South Eastern Desert, Egypt. Advances in Space Research, 65(3), 1008-1024. https://doi.org/10.1016/j.asr.2019.10.030
  5. Abdelkader, M.A., Watanabe, Y., Shebl, A., Badawi, M., Dawoud, M., El-Dokouny, H.A., Csámer, Á., & Abdellatif, M. (2024). Advanced exploration of rare metal mineralization through integrated remote sensing and geophysical analysis of structurally controlled hydrothermal alterations. Journal of Geochemical Exploration, 267, 107598. https://doi.org/10.1016/j.gexplo.2024.107598
  6. Ninomiya, Y. (2003). A stabilized vegetation index and several mineralogic indices defined for ASTER VNIR and SWIR data. Proceedings of the International Geoscience and Remote Sensing Symposium, 3, 1552-1554. https://doi.org/10.1109/IGARSS.2003.1294172
  7. Gabr, S.S., Hassan, S.M., & Sadek, M.F. (2015). Prospecting for new gold-bearing alteration zones at El-Hoteib area, South Eastern Desert, Egypt, using remote sensing data analysis. Ore Geology Reviews, 71, 1-13. https://doi.org/10.1016/j.oregeorev.2015.04.021
  8. Noori, L., Pour, A.B., Askari, G., Taghipour, N., Pradhan, B., Lee, C.W., & Honarmand, M. (2019). Comparison of different algorithms to map hydrothermal alteration zones using ASTER remote sensing data for polymetallic vein-type ore exploration: Toroud-Chahshirin Magmatic Belt (TCMB), North Iran. Remote Sensing, 11(5), 495. https://doi.org/10.3390/rs11050495
  9. Mirsepahvand, F., Jafari, M.R., Afzal, P., & Arian, M.A. (2022). Identification of alteration zones using ASTER data for metallic mineralization in Ahar region, NW Iran. Journal of Mining and Environment, 13(1), 309-324. https://doi.org/10.22044/jme.2022.11477.2135
  10. Pour, A.B., Hashim, M., Park, Y., & Hong, J.K. (2018). Mapping alteration mineral zones and lithological units in Antarctic regions using spectral bands of ASTER remote sensing data. Geocarto International, 33(12), 1281-1306. https://doi.org/10.1080/10106049.2017.1347207
  11. Aboelkhair, H., Ibraheem, M., & Abou El-Magd, I. (2021). Integration of airborne geophysical and ASTER remotely sensed data for delineation and mapping the potential mineralization zones in Hamash area, South Eastern Desert, Egypt. Arabian Journal of Geosciences, 14(12), 1157. https://doi.org/10.1007/s12517-021-07471-y
  12. Beygi, S., Talovina, I.V., Tadayon, M., & Pour, A.B. (2021). Alteration and structural features mapping in Kacho-Mesqal zone, Central Iran using ASTER remote sensing data for porphyry copper exploration. International Journal of Image and Data Fusion, 12(2), 155-175. https://doi.org/10.1080/19479832.2020.1838628
  13. Ghoneim, S.M., Ali, H.F., Abdelrahman, K., Fnais, M.S., & Lala, A.M.S. (2024). Integrating remote sensing and geophysical data for mapping potential gold mineralization localities at Abu Marawat area, Central Eastern Desert, Egypt. Scientific Reports, 14(1), 18273. https://doi.org/10.1038/s41598-024-68921-7
  14. Mahdi, A.M., Eldosouky, A.M., El Khateeb, S.O., Youssef, A.M., & Saad, A.A. (2022). Integration of remote sensing and geophysical data for the extraction of hydrothermal alteration zones and lineaments; Gabal Shilman basement area, Southeastern Desert, Egypt. Journal of African Earth Sciences, 194, 104640. https://doi.org/10.1016/j.jafrearsci.2022.104640
  15. Ali, H.F., & Ghoneim, S.M. (2022). Satellite-based silica mapping as an essential mineral for clean energy transition: Remote sensing mineral exploration as a climate change adaptation approach. Journal of African Earth Sciences, 196, 104683. https://doi.org/10.1016/j.jafrearsci.2022.104683
  16. Shebl, A., Abdellatif, M., Badawi, M., Dawoud, M., Fahil, A.S., & Csámer, Á. (2023). Towards better delineation of hydrothermal alterations via multi-sensor remote sensing and airborne geophysical data. Scientific Reports, 13(1), 7406. https://doi.org/10.1038/s41598-023-34531-y
  17. Ali, H.F., Abu El Ata, A.S.A., Youssef, M.A.S., Salem, S.M., & Ghoneim, S.M. (2023). A newly developed multi-algorithm integration technique for mapping the potentially mineralized alteration zones. The Egyptian Journal of Remote Sensing and Space Sciences, 26(3), 691-711. https://doi.org/10.1016/j.ejrs.2023.07.008
  18. Asadzadeh, S., Zhou, X., & Chabrillat, S. (2024). Assessment of the spaceborne EnMAP hyperspectral data for alteration mineral mapping: A case study of the Reko Diq porphyry Cu-Au deposit, Pakistan. Remote Sensing of Environment, 314, 114389. https://doi.org/10.1016/j.rse.2024.114389
  19. Asadzadeh, S., Chabrillat, S., Cudahy, T., Rashidi, B., & de Souza Filho, C.R. (2024). Alteration mineral mapping of the Shadan porphyry Cu-Au deposit (Iran) using airborne imaging spectroscopic data: Implications for exploration drilling. Economic Geology, 119(1), 139-160. https://doi.org/10.5382/econgeo.5041
  20. Ghoneim, S.M., Wang, C., Abdelrahman, K., & Ali, H.F. (2025). Mapping alteration zones at Kab Amiri area, Egypt: New insights from the EnMAP hyperspectral satellite data. Journal of African Earth Sciences, 228, 105666. https://doi.org/10.1016/j.jafrearsci.2025.105666
  21. Ghoneim, S.M., Wang, C., Ali, H.F., & Hamimi, Z. (2025). Detecting gold-bearing alteration zones using remote sensing data (Hyperion, ASTER and Landsat-9): A case study from Sodmein area, Central Egyptian Nubian Shield. Gold deposits in Egypt: Geology, Settings, Types, Genesis and Spatiotemporal Distribution, 405-423. https://doi.org/10.1007/978-3-031-75972-7_15
  22. Mbianya, G.N., Ngnotue, T., Wambo, J.D.T., Ganno, S., Pour, A.B., Kenne, P.A., Fossi, D.H., & Wolf, I.D. (2021). Remote sensing satellite-based structural/alteration mapping for gold exploration in the Ketté goldfield, Eastern Cameroon. Journal of African Earth Sciences, 184, 104386. https://doi.org/10.1016/j.jafrearsci.2021.104386
  23. Yao, F., Xu, X., Yang, J., & Geng, X. (2021). A remote-sensing-based alteration zonation model of the Duolong porphyry copper ore district, Tibet. Remote Sensing, 13(24), 5073. https://doi.org/10.3390/rs13245073
  24. Mamouch, Y., Attou, A., Miftah, A., Ouchchen, M., Dadi, B., Achkouch, L., Et-tayea, Y., Allaoui, A., Boualoul, M., Randazzo, G., Lanza, S., & Muzirafuti, A. (2022). Mapping of hydrothermal alteration zones in the Kelâat M’Gouna region using airborne gamma-ray spectrometry and remote sensing data: Mining implications (Eastern Anti-Atlas, Morocco). Applied Sciences, 12(3), 957. https://doi.org/10.3390/app12030957
  25. Hamed, M., Khaled, M.A., Said, R.E.M., Ghoneim, S.M., Saad, E., Abd El-Aal, M., & Sayed, A.E.D.H. (2024). Patterns distribution, concentrations and sources of radioactive elements from black sand in the Red Sea coast, Egypt. Journal of Hazardous Materials, 480, 136051. https://doi.org/10.1016/j.jhazmat.2024.136051
  26. Mohammed, A.H., Khalifa, A.M., Mohamed, H.M., Abd El-Wahid, K.H., & Hanafy, M.H. (2024). Assessment of heavy metals at mangrove ecosystem, applying multiple approaches using in-situ and remote sensing techniques, Red Sea, Egypt. Environmental Science and Pollution Research, 31(5), 8118-8133. https://doi.org/10.1007/s11356-023-31625-y
  27. Amer, R., El Mezayen, A., & Hasanein, M. (2016). ASTER spectral analysis for alteration minerals associated with gold mineralization. Ore Geology Reviews, 75, 239-251. https://doi.org/10.1016/j.oregeorev.2015.12.008
  28. Zoheir, B., Steele-MacInnis, M., & Garbe-Schönberg, D. (2019). Orogenic gold formation in an evolving, decompressing hydrothermal system: Genesis of the Samut gold deposit, Eastern Desert, Egypt. Ore Geology Reviews, 105, 236-257. https://doi.org/10.1016/j.oregeorev.2018.12.030
  29. ElGalladi, A., Araffa, S., Mekkawi, M., & Abd-AlHai, M. (2022). Exploring mineralization zones using remote sensing and aeromagnetic data, West Allaqi area, Eastern-Desert, Egypt. The Egyptian Journal of Remote Sensing and Space Sciences, 25(2), 417-433. https://doi.org/10.1016/j.ejrs.2022.03.007
  30. Araffa, S.A.S., Abd-AlHai, M.M., Mekkawi, M.M., & ElGalladi, A.A.M. (2022). Integrated geophysical, remote sensing and geochemical investigation to explore gold-mineralizations and mapping listvenites at Wadi Haimur, Eastern Desert, Egypt. Geocarto International, 37(27), 17565-17602. https://doi.org/10.1080/10106049.2022.2129838
  31. Shebl, A., & Hamdy, M. (2023). Multiscale (microscopic to remote sensing) preliminary exploration of auriferous-uraniferous marbles: A case study from the Egyptian Nubian Shield. Scientific Reports, 13(1), 173. https://doi.org/10.1038/s41598-023-36388-7
  32. Shebl, A., Abdellatif, M., Abriha, D., Dawoud, M., Hussein Ali, M.A., Mahmoud, A.S., Kristály, F., & Csámer, Á. (2025). EnMap hyperspectral data in geological investigations: Evaluation for lithological and hydrothermal alteration mapping in Neoproterozoic rocks. Gondwana Research, 143, 91-124. https://doi.org/10.1016/j.gr.2025.03.014
  33. Egyptian Geological Survey and Mining Authority. (1992). Geologic map of Al Qusayr quadrangle, Egypt (Sheet NG-36-K, Scale 1:250000). Geologic map.
  34. Hamimi, Z., & Abd El-Wahed, M.A. (2020). Suture(s) and major shear zones in the Neoproterozoic basement of Egypt. The geology of Egypt, 153-189. https://doi.org/10.1007/978-3-030-15265-9_5
  35. Rodriguez-Galiano, V.F., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (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
  36. Wang, S., Zhou, K., Wang, J., & Zhao, J. (2022). Identifying and mapping alteration minerals using HySpex airborne hyperspectral data and random forest algorithm. Frontiers in Earth Science, 10, 871529. https://doi.org/10.3389/feart.2022.871529
  37. Mohamed Taha, A.M., Xi, Y., He, Q., Hu, A., Wang, S., & Liu, X. (2023). Investigating the capabilities of various multispectral remote sensors data to map mineral prospectivity based on random forest predictive model: A case study for gold deposits in Hamissana area, NE Sudan. Minerals, 13(1), 49. https://doi.org/10.3390/min13010049
  38. Zheng, C., Yuan, F., Luo, X., Li, X., Liu, P., Wen, M., Chen, Z., & Albanese, S. (2023). Mineral prospectivity mapping based on support vector machine and random forest algorithm: A case study from the Ashele copper-zinc deposit, Xinjiang, NW China. Ore Geology Reviews, 159, 105567. https://doi.org/10.1016/j.oregeorev.2023.105567
  39. Green, A.A., Berman, M., Switzer, P., & Craig, M.D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1), 65-74. https://doi.org/10.1109/36.3001
  40. Savitzky, A., & Golay, M.J.E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639. https://doi.org/10.1021/ac60214a047
  41. Kalinowski, A., & Oliver, S. (2004). ASTER mineral index processing manual.Retrived from:https://pid.geoscience.gov.au/dataset/ga/67973

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