Zijing Luo

Zijing Luo

Visiting Overseas Student

China University of Geosciences

Email: 1201911208@cug.edu.cn; zijing.luo@sydney.edu.au

Research Project

Interpretable deep learning algorithm for Mineral Prospectively Mapping

Project Summary

Deep learning (DL) algorithms have been proven to be powerful tools for mining complex and non-linear geospatial data and extracting unknown patterns of geological processes. In mineral exploration, DL methods have been adopted to enhance the extraction of mineralization information and recognize hidden patterns (Zuo et al., 2019). Despite the excellent performance of DL in mineral prospectivity mapping (MPM), their nested nonlinear network structures and deep abstraction expression make such models highly opaque (Castelvecchi, 2016; Samek et al., 2017; Reichstein et al., 2019). This opaque black-box property prevents validating, interpreting, and understanding the reasoning of the model (Reichstein et al., 2019). As a result, the decision results of end-to-end DL models cannot be fully trusted. Understanding the meaning behind the features automatically extracted by a DL algorithm, and explaining the decisions made by a DL algorithm are from current research hotspots (Ribeiro et al., 2016; Karpatne et al., 2017a; Raissi, 2018). In addition, although the data-driven DL algorithm can increase the accuracy of model output through multiple trainings, it may not accurately model the metallogenic regularity due to the lack of geological information constraints. This means that even if the model performs well on the train set or the test set, it may have large deviations in situations outside the valid domain (Karpatne et al., 2017a, 2017b). That is, there is a phenomenon that is inconsistent with the actual geological prior knowledge (Reichstein et al., 2019). My research project focuses on two approaches involving both post-hoc interpretability analysis and ad-hoc interpretable modeling to improve the interpretability of the DL algorithm applied to MPM. And the metallogenic model and DL algorithm are combined to make the delineated metallogenic prospect area consistent with geological knowledge.

Supervisors

Prof Dietmar Müller (The University of Sydney).

Dr Ehsan Farahbakhsh (The University of Sydney).

Prof Renguang Zuo (China University of Geosciences).

Research Funding

China Scholarship Council (http://ww.csc.edu.cn).

Publications

• Luo, Z., Zuo, R., Xiong, Y., & Zhou, B. (2023). Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms. Natural Resources Research, 1-19.
• Luo, Z., Zuo, R., & Xiong, Y. (2022). Visual interpretable deep learning algorithm for geochemical anomaly recognition. Natural Resources Research, 31(5), 2211-2223.
• Luo, Z., Zuo, R., Xiong, Y., & Wang, X. (2021). Detection of geochemical anomalies related to mineralization using the GANomaly network. Applied Geochemistry, 131, 105043.
• Luo, Z., Xiong, Y., & Zuo, R. (2020). Recognition of geochemical anomalies using a deep variational autoencoder network. Applied Geochemistry, 122, 104710.

References

Castelvecchi, D. (2016). Can we open the black box of AI?. Nature News, 538(7623), 20.

Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017a). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on knowledge and data engineering, 29(10), 2318-2331.

Karpatne, A., Watkins, W., Read, J., & Kumar, V. (2017b). Physics-guided neural networks (PGNN): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431.

Raissi, M. (2018). Deep hidden physics models: Deep learning of non-linear partial differential equations. The Journal of Machine Learning Research, 19(1), 932-955.

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat, F. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144.

Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.

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.