133 [15] Yousefi, M.; Carranza, E. J. M.; Kreuzer, O. P.; Nykänen, V.; Hronsky, J. M. A.; Mihalasky, M. J. (2021). Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook. Journal of Geochemical Exploration. 229, 106839. [16] Yang, F.; Zuo, R.; Kreuzer, O. P. (2024). Artificial intelligence for mineral exploration: A review and perspectives on future directions from data science. Earth-Science Reviews. 258, 104941. [17] Zuo, R. (2020). Geodata Science-Based Mineral Prospectivity Mapping: A Review. Natural Resources Research. 29(6), 3415–3424. [18] Chen, Y.; Wu, W. (2017). Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data. Australian Journal of Earth Sciences. 64(5), 639–651. [19] Xiong, Y.; Zuo, R. (2021). A positive and unlabeled learning algorithm for mineral prospectivity mapping. Computers & Geosciences. 147, 104667. [20] Hronsky, J. M. A.; Kreuzer, O. P. (2019). Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future. Ore Geology Reviews. 107, 647–653. [21] Mou, N.; Carranza, E. J. M.; Wang, G.; Sun, X. (2023). A Framework for Data-Driven Mineral Prospectivity Mapping with Interpretable Machine Learning and Modulated Predictive Modeling. Natural Resources Research. 32(6), 2439–2462. [22] Partington, G. A.; Sale, M. J. (2004). Prospectivity mapping using GIS with publicly available earth science data – A new targeting tool being successfully used for exploration in New Zealand. PACRIM 2004 Congress Volume. [23] Yu, X.; Yu, P.; Wang, K.; Cao, W.; Zhou, Y. (2024). Data-Driven Mineral Prospectivity Mapping Based on Known Deposits Using Association Rules. Natural Resources Research. 33(3), 1025–1048. [24] Buckingham, A.; Dentith, M.; List, R. (2003). Towards a system for content-based magnetic image retrieval. Exploration Geophysics. 34(3), 195–206. [25] Zhao, J.; Wang, W.; Cheng, Q.; Agterberg, F. (2016). Mapping of Fe mineral potential by spatially weighted principal component analysis in the eastern Tianshan mineral district, China. Journal of Geochemical Exploration. 164, 107–121. [26] Mou, N.; Carranza, E. J. M.; Xue, J.; Zhang, S.; Wang, G.; Song, H.; Chen, Y.; Ren, X. (2025). Interpretable machine learning for mineral prospectivity mapping in the Qulong–Jiama district, Tibet, China. Ore Geology Reviews. 182, 106659.
RkJQdWJsaXNoZXIy MTM0Mzk2