熊义辉，副研究员，硕士生导师，1991年生，国际SCI期刊Journal of Geochemical Exploration副主编，国际数学地球科学协会（IAMG）会员。长期围绕复杂地质条件下深层次矿化信息挖掘与集成等前缘科学问题与技术难题，开展数学地球科学、大数据、机器学习（深度学习）、计算机模拟与矿产勘查的多学科交叉研究。研究成果在Mathematical Geosciences、Chemical Geology、Computers & Geosciences、Ore Geology Reviews、Applied Geochemistry、Journal of Geochemical Exploration等期刊上发表SCI收录论文20余篇，其中第一作者SCI论文11篇。

招生方向

机器学习（深度学习）、地学大数据挖掘、矿产资源定量预测与评价、成矿动力学过程模拟等方向，欢迎具有地探、矿普、遥感、GIS或类似专业背景，兼具一定编程能力/兴趣（Matlab/Python/C++）的本科生报考。

科研项目

1. 国家自然科学基金青年基金，基于元胞自动机的构造-流体耦合成矿作用模拟及其自组织临界性研究，2021/1-2023/12，主持；

2. 中国地质大学（武汉）“地大学者”青年优秀人才计划项目，2019/7-2022/7，主持；

3. 国家自然科学基金面上项目，基于RX多元勘查地球化学数据融合与异常识别，2018/1-2021/12，参与；

4. 国家自然科学基金优秀青年科学基金，数学地质，2016/1-2018/12，参与；

发表论文（详见：https://scholar.google.com/citations?hl=zh-CN&user=b7NMmMYAAAAJ）

22. Luo, Z., Zuo, R., Xiong, Y., & Wang, X. (2021). Detection of geochemical anomalies related to mineralization using the GANomaly network. Applied Geochemistry, 105043.

21. Zhang, C., Zuo, R., & Xiong, Y. (2021). Detection of the multivariate geochemical anomalies associated with mineralization using a deep convolutional neural network and a pixel-pair feature method. Applied Geochemistry, 104994.

20. Zuo, R., Kreuzer, O. P., Wang, J., Xiong, Y., Zhang, Z., & Wang, Z. (2021). Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions. Natural Resources Research.https://doi.org/10.1007/s11053-021-09871-z

19. Xiong, Y., & Zuo, R. (2021). Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder. Mathematical Geosciences, 1-22.

18. Xiong, Y., & Zuo, R. (2021). A positive and unlabeled learning algorithm for mineral prospectivity mapping. Computers & Geosciences, 147, 104667.

17. Luo, Z., Xiong, Y., & Zuo, R. (2020). Recognition of geochemical anomalies using a deep variational autoencoder network. Applied Geochemistry, 122, 104710.

16. Xiong, Y., & Zuo, R. (2020). Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine. Computers & Geosciences, 140, 104484.

15. Li, T., Zuo, R., Xiong, Y., & Peng, Y. (2020). Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping. Natural Resources Research, 1-12.

14. Xiong, Y., Zuo, R., Clarke, K. C., Miller, S. A., & Wang, J. (2020). Modeling singular mineralization processes due to fluid pressure fluctuations. Chemical Geology, 535, 119458.

13. Zuo, R., & Xiong, Y. (2020). Geodata science and geochemical mapping. Journal of Geochemical Exploration, 209, 106431.

Wang, J., Zuo, R., & Xiong, Y. (2020). Mapping mineral prospectivity via semi-supervised random forest. Natural Resources Research, 29(1), 189-202.

12. Xiong, Y., Zuo, R., & Clarke, K. C. (2019). A fractal model of granitic intrusion and variability based on cellular automata. Computers & Geosciences, 129, 40-48.

11. 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.

10. Chen, L., Guan, Q., Xiong, Y., Liang, J., Wang, Y., & Xu, Y. (2019). A Spatially Constrained Multi-Autoencoder approach for multivariate geochemical anomaly recognition. Computers & Geosciences, 125, 43-54.

9. 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.

8. Xiong, Y., Zuo, R., Wang, K., & Wang, J. (2018). Identification of geochemical anomalies via local RX anomaly detector. Journal of Geochemical Exploration, 189, 64-71.

7. Xiong, Y., & Zuo, R. (2018). GIS-based rare events logistic regression for mineral prospectivity mapping. Computers & Geosciences, 111, 18-25.

6. Zuo, R., & Xiong, Y. (2018). Big data analytics of identifying geochemical anomalies supported by machine learning methods. Natural Resources Research, 27(1), 5-13.

5. Xiong, Y., & Zuo, R. (2017). Effects of misclassification costs on mapping mineral prospectivity. Ore Geology Reviews, 82, 1-9.

4. Xiong, Y., & Zuo, R. (2016). A comparative study of two modes for mapping felsic intrusions using geoinformatics. Applied Geochemistry, 75, 277-283.

3. Xiong, Y., & Zuo, R. (2016). Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86, 75-82.

2. Gao, Y., Zhang, Z., Xiong, Y., & Zuo, R. (2016). Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China. Ore Geology Reviews, 75, 16-28.

1. Zhang, Z., Zuo, R., & Xiong, Y. (2016). A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China. Science China Earth Sciences, 59(3), 556-572.