一、个人简介

刘岳，副教授，博士生导师，地球探测与信息技术专业，加拿大约克大学访问学者，入选“地大学者”青年拔尖人才、 自然资源部高层次科技创新人才工程、武汉英才计划等。主要从事数学地质与矿产勘查相关的学科交叉研究与教学工作，研究方向包括矿床预测及不确定性评价、勘查地球化学、地学大模型研发与应用、三维地质建模与找矿预测等。主持中国博士后基金一等资助与特别资助、国家自然科学基金青年基金与面上基金、国家重点研发计划专题等10多项、教改项目2项，发表SCI论文30余篇。担任Journal of Geochemical Exploration、Geochemistry | Chemie der Erde、Natural Resources Research、Geochemistry: Exploration-Environment-Analysis等期刊副主编、编委，主编SCI专辑2期。获省级科学技术进步奖一等奖和三等奖各1项。讲授《数学地质》、《数字地质学》、《资源信息工程》、《秭归地质实习》、《北戴河地质认识实习》等多门本科生与研究生课程。获评“国家大学生创新创业计划优秀指导老师”、“中国地质大学优秀硕士论文指导老师”、“中国地质大学优秀本科论文指导老师”、“中国地质大学优秀班主任”等奖项和荣誉称号。

个人主页: https://www.researchgate.net/profile/Yue-Liu-71

二、发表论文

l Liu, Y., 2026. A spatially aware evidential deep learning framework for mineral prospectivity mapping and uncertainty evaluation. Mathematical Geosciences, pp.1-32.

l Liu, Y., 2025. Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework. Artificial Intelligence in Geosciences, 6(2), p.100164.

l Liu, Y., Zhang, D., Li, Z., Fan, H. and Peng, W., 2025. Dirichlet-based uncertainty-aware deep learning for explainable mineral prospectivity mapping. Natural Resources Research, pp.1-18.

l  Zhang, L., Liu, Y^*., Zhang, K., Zha, Z., Wu, J., Xuan, W., Zhang, H., 2025. Compositional balance analysis for geochemical prospectivity mapping in Nenjiang–Heihe region, China. Geochemistry, p.126375.

l Lou Y., Liu Y^*, 2025. Mineral prospectivity mapping based on a novel self-ensembling graph convolutional network. Mathematical Geosciences, 57, 629-656.

l Wu, S., Liu, Y^*., 2025. Interpretable dual-channel convolutional neural networks for lithology identification based on multisource remote sensing data. Remote Sensing, 17(7), p.1314.

l Liu, Y., Xia, Q., Duan, J., Dai, J., Wu, S., Zhao, Z., 2024. Geochemical anomalies of critical metals in the Eastern Kunlun Orogenic Belt, China: Implications for nickel and cobalt mineral exploration. Ore Geology Reviews, p.106168.

l Fang, H., Liu, Y^*., Zhang, Q., 2024. Graph convolutional network for lithological classification and mapping using stream sediment geochemical data and geophysical data. Geochemistry: Exploration, Environment, Analysis, pp.geochem2024-006.

l Liu, Y., Xia, Q., 2024. Compositional balance analysis: a novel method for tectono-geochemical anomaly identification and blind ore deposit prediction. Applied Geochemistry, 164, 105939.

l Zhang, Q., Liu, Y^*., Fang, H., 2024. Manifold learning-based UMAP method for geochemical anomaly identification. Geochemistry, p.126157.

l Liu, Y^*., Xia, Q., Cheng, Q., 2023. Sequential Gaussian co-simulation of tectono-geochemical anomaly for concealed ore deposit prediction. Applied Geochemistry, 157, 105768.

l  Jiang, X., Wang, X., Liu, Y^*., Carranza, E. J. M., Xie, S., Wan, X., 2023. Spatial extrapolation of downscaled geochemical data using conditional GAN. Computers & Geosciences, 179, 105420.

l  Lou, Y., Liu Y^*., 2023. Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China. Earth and Space Science, 10, e2022EA002596.

l  Liu, Y., 2022. How to determine the optimal balance for geochemical pattern recognition and anomaly mapping based on compositional balance analysis? Geochemistry: Exploration, Environment, Analysis, 22(3), geochem2022-009.

l  Liu, Y., Carranza, E. J. M., Xia, Q., 2022. Developments in quantitative assessment and modeling of mineral resource potential: an overview. Natural Resources Research, 31(4), 1825-1840.

l Liu, Y., Carranza, E. J. M., 2022. Uncertainty analysis of geochemical anomaly by combining sequential indicator Co-simulation and local singularity analysis. Natural Resources Research, 31(4), 1889-1908.

l    Liu, Y.,Xia, Q., Cheng, Q., 2021. Aeromagnetic and geochemical signatures in the Chinese Western Tianshan: Implications for tectonic setting and mineral exploration. Natural Resources Research, 30(5), 3165-3195.

l  Liu, Y^*.,  Carranza, E. J. M., Zhou, K., Xia, Q., 2019. Compositional balance analysis: An elegant method of geochemical pattern recognition and anomaly mapping for mineral exploration. Natural Resources Research, 28(4), 1269-1283.

l   Liu, Y., Cheng, Q., Carranza, E. J. M., Zhou, K., 2019. Assessment of geochemical anomaly uncertainty through geostatistical simulation and singularity analysis. Natural Resources Research, 28(1), 199-212. 

l Liu, Y., Cheng, Q., Zhou, K., 2019. New insights into element distribution patterns in geochemistry: a perspective from fractal density. Natural Resources Research, 28(1), 5-29.

l  Liu, Y.,Xia, Q., Carranza, E. J. M., 2019. Integrating sequential indicator simulation and singularity analysis to analyze uncertainty of geochemical anomaly for exploration targeting of tungsten polymetallic mineralization, Nanling belt, South China. Journal of Geochemical Exploration, 197, 143–158.

l  刘岳,2019.基于随机模拟技术和局部奇异性理论的地球化学异常不确定性分析.地质与勘探,55(6),1416-1425.

l  刘岳,周可法,2018.西准噶尔成矿带金矿异常识别及其不确定性分析.地球科学,43(9),3186-3199.

l  Liu, Y., Zhou K., Zhang N., Wang J., 2018. Maximum entropy modeling for orogenic gold prospectivity mapping in the Tangbale-Hatu belt, western Junggar, China. Ore Geology Reviews, 100, 133–147.

l Liu, Y, Zhou, K., Xia, Q., 2018. A MaxEnt model for mineral prospectivity mapping. Natural Resources Research, 27(3), 299-313.

l  Liu, Y., Zhou, K., Emmanuel John M. Carranza, 2018. Compositional balance analysis for geochemical pattern recognition and anomaly mapping in the western Junggar region, China. Geochemistry-Exploration Environment Analysis, 18, 263–276.

l  Liu, Y., Zhou, K., Cheng, Q., 2017. A new method for geochemical anomaly separation based on the distribution patterns of singularity indices. Computers & Geosciences, 105, 139-147.

l  Liu, Y., Cheng, Q., Xia, Q., Wang, X., 2015. The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China. Frontiers of Earth Science, 9(2), 342–354.

l  Liu, Y., Cheng, Q., Xia, Q., Wang, X., 2014. Identification of REE mineralization-related geochemical anomalies using fractal/multifractal methods in the Nanling belt, South China. Environmental Earth Sciences, 72 (12), 5159–5169.

l Liu, Y., Cheng, Q., Xia, Q., Wang, X., 2014. Multivariate analysis of stream sediment data from Nanling metallogenic belt, South China. Geochemistry: Exploration, Environment, Analysis, 14(4), 331-340.

l  Liu, Y., Cheng, Q., Xia, Q., Wang, X., 2014. Mineral potential mapping for tungsten polymetallic deposits in the Nanling metallogenic belt, South China. Journal of Earth Science, 25(4), 689–700.

l  刘岳,陈翠华,何彬彬,2011.基于证据权模型的东昆仑五龙沟金矿潜力预测.中国矿业大学学报, 40(2),306-312.

三、专利

l  刘岳. 一种贝叶斯深度学习的矿产预测与勘查风险评价方法. 中国，授权专利号：ZL202510050420.9，2025年12月19日.

l  刘岳. 一种构造地球化学协同模拟的隐伏矿勘查风险评价方法. 中国，授权专利号：ZL202310370616.7，2025年02月14日.

l  刘岳, 方豪, 张庆腾. 一种融合GAT和GCN的矿产资源智能评价方法. 中国，授权专利号：ZL202311623504.4，2024年12月20日.

l  刘岳. 隐伏矿探测的构造地球化学组合异常识别方法. 中国，授权专利号：ZL202211273541.2，2024年12月17日.

l  一种基于高精度产状模拟的三维地质MPS建模方法. 中国，申请号：202511940547.4.

l  一种空间感知的概率神经网络矿产预测方法. 中国，申请号：202510920893.X.

l  不确定性感知的深度学习矿产预测方法. 中国，申请号：202510759584.9.

l  一种基于双通道卷积神经网络的岩性识别方法.中国，申请号：202411338278.X.

四、软著

l  矿产资源智能预测与评价系统[简称：MinProMapAI].计算机软件著作权,登记号：2024SR0568384,国家版权局, 2024-4-26.

l  成分地球化学数据分析系统[简称：ComGeochemDa]，计算机软件著作权,授权号：2024SR0747383, 国家版权局. 2024-5-31.