Track 1: AI and Data-Driven Decision Making

Further work is required to refine the latent space representation, incorporate additional data sources, and evaluate the approach across different geological settings and deposit types. ACKNOWLEDGEMENTS The author gratefully acknowledges the Yukon Geological Survey for providing access to the geoscientific datasets that made this study possible. The author also acknowledges the Alaska Satellite Facility for providing access to Sentinel-1 SAR data. REFERENCES An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. Proceedings of the 2nd International Conference on Machine Learning (ICML) Workshop on Deep Learning for Anomaly Detection. http://dm.snu.ac.kr/static/docs/TR/SNUDM-TR-2015-03.pdf Baur, C., Wiestler, B., Albarqouni, S., & Navab, N. (2020). Scale-space autoencoders for unsupervised anomaly segmentation in brain MRI. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). https://arxiv.org/abs/2006.12852 Chen, X., Pawlowski, N., Rajchl, M., Glocker, B., & Konukoglu, E. (2018). Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv preprint. https://arxiv.org/abs/1806.04972 He, H., Zhu, H., Yang, X., et al. (2024). Mineral prospectivity prediction based on convolutional neural network and ensemble learning. Scientific Reports, 14, Article 22654. https://doi.org/10.1038/s41598-024-73357-0 International Energy Agency. (2021). The role of critical minerals in clean energy transitions. IEA Publications. https://www.iea.org/reports/the-role-of-critical-minerals-in-clean-energytransitions Kingma, D. P., & Welling, M. (2014). Auto-encoding variational bayes. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1312.6114 LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278– 2324. https://doi.org/10.1109/5.726791 Sillitoe, R. H. (2010). Porphyry copper systems. Economic Geology, *105*(1), 3– 41. https://doi.org/10.2113/gsecongeo.105.1.3 Singer, D. A., & Kouda, R. (1999). Examining risk in mineral exploration. Natural Resources Research, 8(2), 111–122. https://doi.org/10.1023/A:1021838618750 Sun, T., Chen, F., Zhong, L., Liu, W., & Wang, Y. (2019). GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China. Ore Geology Reviews, 109, 26– 49. https://doi.org/10.1016/j.oregeorev.2019.04.003 Xiong, Y., Zuo, R., & Carranza, E. J. M. (2018). Mapping mineral prospectivity through big data analytics and a deep learning algorithm. Ore Geology Reviews, 102, 811– 817. https://doi.org/10.1016/j.oregeorev.2018.10.006

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