Machine learning model to assess gold mineralization potential mapping for northwest Thanh Hoa province

• Autorzy: Truong Xuan Quang , Tran Van Anh , Truong Zuan Luan

Identyfikatory

DOI

10.29227/IM-2025-01-02-033

• Konferencja: CESD 2024 : Conference on Earth Sciences : November 11th, 2024, Ho Chi Minh City, Vietnam
• Języki publikacji: EN

Abstrakty

EN

In Vietnam, gold is one of the key minerals for mining. While gold mining in Thanh Hoa province holds economic value, evaluating and predicting its spatial distribution remains challenging. Machine learning is becoming a powerful tool in the field of mineral research and extraction, including gold. The strength of Machine Learning lies in its ability to process and analyze large amounts of data to make more accurate predictions, optimize exploration and extraction processes, and minimize risks. This paper presents a set of machine learning models to identify the best model for generating a gold deposit potential map. The study utilized seven thematic maps, including lithology, magnetic data, gravity data, geological age, faults, lineaments, ore point density, magma distribution, gold mineral potential from remote sensing imagery, and placer gold distribution, as input data for the models. Additionally, 706 points (353 sampling sites with gold placer and 353 sites without gold) were used to generate the training and testing dataset. The study area is situated in the northwestern part of Thanh Hoa province, known for its high potential for gold deposits. Machine learning models such as Random Forest, Logistic Regression, SVM, and Gradient Boosting were implemented to identify the best-fit model for the study area. After comparing the models, the initial results showed that the Random Forest model achieved the highest accuracy with an AUC of 0.82, identifying 4% of the area as having very high potential. The final result, a gold mineral potential map, was compared with field data, and it was found that all points containing gold in the field were located in the areas (very high potential) in the prediction map.

Słowa kluczowe

EN

gold mineral potential mapping; machine learning; northwest Thanh Hoa province

PL

mapowanie potencjału złota; uczenie maszynowe; północno-zachodnia prowincja Thanh Hoa

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2025
• Tom: Vol. 2, no. 1
• Strony: art. no. 33
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr., zdj.

Twórcy

autor

Truong Xuan Quang

• Faculty of Architecture, Urban Design and Sustainable Sciences, VNU School of Interdisciplinary Sciences and Arts, Vietnam National University, Hanoi, Vietnam

autor

Tran Van Anh

• Department of Photogrammetry and Remote Sensing, Hanoi University of Mining and Geology, Hanoi, Vietnam

autor

Truong Zuan Luan

• Faculty of Information Technology, Hanoi University of Mining and Geology, Hanoi, Vietnam

Bibliografia

• 1. Yixiao Wu, Bingli Liu, Yaxin Gao, Cheng Li, Rui Tang, Yunhui Kong, Miao Xie, Kangning Li, Shiyao Dan, Ke Qi, Yufei Ren, Zhuo Wu, Mineral prospecting mapping with conditional generative adversarial network augmented data, Ore Geology Reviews, Volume 163, 2023, 105787, ISSN 0169-1368, https://doi.org/10.1016/j.oregeorev.2023.105787.
• 2. Lee, Saro & Oh, Hyun-Joo. (2011). Application of Artificial Neural Network for Mineral Potential Mapping. 10.5772/16187.
• 3. Xiong, Y., & Zuo, R. (2018). GIS-based rare events logistic regression for mineral prospectivity mapping. Computers & Geosciences, 111, 18-25. doi:10.1016/j.cageo.2017.10.005
• 4. Zhang, D., Ren, N., & Hou, X. (2018). An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping. Geoscientific Model Development, 11(6), 2525-2539. doi:10.5194/gmd-11-2525-201
• 5. Zhao PD (2007) Quantitative mineral prediction and deep mineral exploration. Earth Sci Front 14(5):1–10. https://doi.org/10.3321/j.issn:1005-2321.2007.05.001. (in Chinese with English abstract)
• 6. Emmanuel John M. Carranza, Alice G. Laborte, Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines),- Computers & Geosciences, Volume 74, 2015, Pages 60-70, ISSN 0098-3004, https://doi.org/10.1016/j.cageo.2014.10.004.
• 7. Emmanuel John M. Carranza, Alice G. Laborte, Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm, Ore Geology Reviews,Volume 71, 2015, Pages 777-787, ISSN 0169-1368, https://doi.org/10.1016/j.oregeorev.2014.08.010
• 8. Chen, M., Xiao, F. Projection Pursuit Random Forest for Mineral Prospectivity Mapping. Math Geosci 55, 963–987 (2023). https://doi.org/10.1007/s11004-023-10070-0
• 9. Porwal, A., Carranza, E. J. M., & Hale, M. (2006). Bayesian network classifiers for mineral potential mapping. Computers & Geosciences, 32, 1-16. https://doi.org/10.1016/j.cageo.2005.03.018
• 10. Renguang Zuo, Emmanuel John M. Carranza, Support vector machine: A tool for mapping mineral prospectivity, Computers & Geosciences, Volume 37, Issue 12, 2011, Pages 1967-1975, ISSN 0098-3004, https://doi.org/10.1016/j.cageo.2010.09.014
• 11. Neda Mahvash Mohammadi, Ardeshir Hezarkhani, Application of support vector machine for the separation of mineralised zones in the Takht-e-Gonbad porphyry deposit, SE Iran, Journal of African Earth Sciences, Volume 143, 2018, Pages 301-308, ISSN 1464-343X, https://doi.org/10.1016/j.jafrearsci.2018.02.005.
• 12. Wang, Z., Zuo, R. & Yang, F. Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data. Math Geosci 55, 1035–1058 (2023). https://doi.org/10.1007/s11004-022-10023-z
• 13. Renguang Zuo, Emmanuel John M. Carranza, Support vector machine: A tool for mapping mineral prospectivity, Computers & Geosciences, Volume 37, Issue 12, 2011, Pages 1967-1975, ISSN 0098-3004, https://doi.org/10.1016/j.cageo.2010.09.014
• 14. Alok Porwal, E.J.M. Carranza, M. Hale, Bayesian network classifiers for mineral potential mapping, Computers & Geosciences, Volume 32, Issue 1, 2006, Pages 1-16, ISSN 0098-3004, https://doi.org/10.1016/j.cageo.2005.03.018
• 15. Skabar, A.A. (2005). Mapping mineralization probabilities using multilayer perceptrons. Natural Resources Research, Vol. 14, No. 2, 109-123, ISSN 15207439
• 16. Chen, Y., Lu, L. The Anomaly Detector, Semi-supervised Classifier, and Supervised Classifier Based on K-Nearest Neighbors in Geochemical Anomaly Detection: A Comparative Study. Math Geosci 55, 1011–1033 (2023). https://doi.org/10.1007/s11004-022-10042-w
• 17. Trần Văn Trị, et al., 2009. Geology and Resources Vietnam. NXb Khoa học Tự nhiên và Công nghệ, Hà Nội. Science and Technology Publishing House (book in Vietnames)
• 18. Dovjikov A. E (editor), (1965). Geology of Northern Vietnam (in Vietnammese). Publish House: Science and Technology, Hanoi
• 19. Tran V Tri et al., (2023). Geoloy and Georesources of Vietnam. Youth Public house (book in English). Pp 26 -72; 89-93
• 20. Zhang, N., Zhou, K., Li, D., 2018. Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China. Earth. Sci. Inform., 11, 553-566
• 21. Tao Sun, Fei Chen, Lianxiang Zhong, Weiming Liu, Yun Wang, GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China, Ore Geology Reviews, Volume 109, 2019, Pages 26-49, ISSN 0169-1368, https://doi.org/10.1016/j.oregeorev.2019.04.003
• 22. Phong, T. V., Phan, T. T., Prakash, I., Singh, S. K., Shirzadi, A., Chapi, K., … Pham, B. T. (2019). Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto International, 36(15), 1685–1708. https://doi.org/10.1080/10106049.2019.1665715
• 23. Breiman, L. Random Forests. Machine Learning 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
• 24. Cortes, C., Vapnik, V. Support-vector networks. Mach Learn 20, 273–297 (1995). https://doi.org/10.1007/BF00994018
• 25. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830
• 26. Najafabadi, M.M., Khoshgoftaar, T.M., Villanustre, F. et al. Large-scale distributed L-BFGS . J Big Data 4, 22 (2017). https://doi.org/10.1186/s40537-017-0084-5

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2026).