A Review on the Fifty-Year Development Trajectory and Hotspot Evolution of Mineral Prospectivity Mapping: A Bibliometrics Perspective
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摘要:
矿产勘查是护航国家资源安全与产业供应链稳定的基础性工作，作为矿产勘查的核心环节，矿产预测在大数据与人工智能技术的助推下已实现跨越式发展，成为地球科学中的热门研究领域，积累了大量的研究文献.本文采用文献计量学方法，以国际数学地球科学学会三本会刊在1969年至2025年间发表的935篇矿产预测主题论文为数据源，分析和探讨了矿产预测近五十多年的研究现状、发展轨迹与热点变迁.文献作者、机构和国别的统计结果表明，Carranza E.J.M.和左仁广分别以署名作者和第一/通讯作者的身份成为本领域最高产和高被引的学者，中国是矿产预测领域最大的论文产出国，中国地质大学（武汉）的发文量和总被引频次在全球机构中位居榜首，本研究领域的合作存在较强的地域导向性，高水平、常态化的国际协同研究网络尚未形成.根据关键词的热点变迁将矿产预测研究分为奠基期（1969-1990年）、发展期（1991-2010年）和繁荣期（2011-2025年），不同时期的主题任务和发展轨迹取决于该时代热门技术和算法的发展水平.奠基期是以矿产资源评价任务为主的阶段，对应了地质统计学（变异函数和克里金插值）热度遥遥领先的时期；发展期GIS技术的兴起和广泛应用助力了矿产预测逐渐替代矿产资源评价成为主流科学任务，而繁荣期机器学习算法的盛行则让矿产智能预测成为热度断档领先的研究主题.矿产预测研究最新的热点和发展趋势是从倚重单一高性能预测模型，转向对智能预测模型内部机制的深入探索与优化，利用前沿人工智能技术解决决策过程黑箱属性和样本稀缺等矿产预测的固有瓶颈问题.优越的深度学习算法近年来收获了最高的热度，但经典的浅层学习算法，如擅长处理高维数据及非线性问题的支持向量机和具有强大抗过拟合能力的随机森林，依然因其高度适配小样本矿产预测任务而成为繁荣期本领域学者的热门选择.本研究借助量化统计分析和可视化工具，不仅为理解矿产预测的学科发展脉络提供了宏观和全面的视角，也为把握本领域未来智能预测发展方向提供了参考.
Abstract:Mineral exploration is a fundamental task for safeguarding national resource security and the stability of industrial supply chain. As a core step of mineral exploration, mineral prospectivity mapping (MPM) has undergone transformative development, driven by big data and artificial intelligence, emerging as a prominent research field within Earth science and accumulating a substantial volume of literature. In this study, we employ bibliometric methods to analyze and discuss the research status, developmental trajectory, and hotspot evolution of MPM over the past five decades, based on a dataset of 935 relevant publications from three flagship journals of the International Association for Mathematical Geosciences, spanning from 1969 to 2025. Bibliometric statistics on authors, institutions, and countries reveal that Carranza E.J.M. and Zuo Renguang are the most highly productive and highly cited scholars in the field as listed as author and first/corresponding author, respectively. China is the largest contributor of publications in this field, and the China University of Geosciences (Wuhan) ranks first in global institutions in both publication volume and total citation. The analysis of collaboration networks indicates a strong regional orientation, lacking a high-level and regular international cooperative research network. The evolution of MPM, based on the hotspot analysis of keywords, is divided into three distinct stages, namely the foundation stage (1969-1990), the expansion stage (1991-2010), and the boom stage (2011-2025). The thematic focus and developmental trajectory of each stage are determined by the prevailing technologies and algorithms of the era. The foundation stage, focusing on mineral resource assessment, was dominated by geostatistics (variogram and Kriging). The rise and widespread application of GIS technology during the expansion stage facilitated the shift of MPM into the mainstream scientific task. In the boom stage, the prevalence of machine learning algorithms led to the dominance of intelligent MPM in the thematic tasks. Recent research hotspots and trends indicate a shift from relying solely on high-performance predictive models towards in-depth exploration and optimization of the internal mechanisms of intelligent models. The focus is on leveraging cutting-edge AI technologies to address inherent challenges such as the black-box nature of decision processes and sample scarcity. Although advanced deep learning algorithms have gained significant traction, classic shallow learning algorithms, such as support vector machine, which exhibits great performance in processing high-dimensional data and nonlinear problems, and random forest characterized by its strong resistance to overfitting, remain popular choices among scholars in this field during the boom stage due to their high suitability for few-shot MPM tasks. By leveraging quantitative statistical and visualization tools, this study provides a macro and comprehensive perspective for understanding the development of MPM, and offers critical insights into future research directions of intelligent prediction in MPM.
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Key words:
- mineral prospectivity mapping /
- bibliometrics /
- data-driven /
- machine learning /
- VOSviewer /
- big data
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图 4 前五高产国家的发文数(a), 发文量前五国家的国内合作及国际合作占比(b)
Fig. 4. Publications of top 5 highly productive countries (a), proportion of domestic and international collaborations for the top 5 highly productive countries (b)
图 7 高频关键词在三个发展期的出现频率
a. 研究任务与对象关键词；b. 技术手段关键词；c. 具体算法关键词
Fig. 7. Frequency ratios of high-frequency keywords in three development periods
图 8 高频关键词的频数累积折线
a. 研究任务与对象关键词；b. 技术手段关键词；c. 具体算法关键词
Fig. 8. Cumulative lines of frequencies of high-frequency keywords
图 9 矿产预测领域近三年新兴热门关键词
Fig. 9. Newly emerging hotspot keywords in the domain of mineral prospectivity mapping in the most recent three years
表 1 高产作者发文量和被引频数统计
Table 1. Publication volume and citations of highly productive authors
作者 国家 所属机构（2025） 发文量 累计被引频数 Carranza E. J. M. South Africa University of the Free State 70 5 035 Zuo Renguang China China University of Geosciences（中国地质大学(武汉)） 54 3 201 Cheng Qiuming China China University of Geosciences（中国地质大学(北京)） 31 2 148 Xiong Yihui China China University of Geosciences（中国地质大学(武汉)） 21 1 580 Wang Gongwen China China University of Geosciences（中国地质大学(北京)） 19 388 Agterberg F. P. Canada Geological Survey of Canada 17 1 376 Parsa Mohammad Canada Geological Survey of Canada 17 355 Mao Xiancheng China Central South University（中南大学） 15 445 Deng Hao China Central South University（中南大学） 15 267 Pan Guocheng China China Jiliang University（中国计量大学） 15 233 
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表 2 高产第一作者/通讯作者发文量和被引频数统计
Table 2. Publications and citations of highly productive authors served as first or corresponding author
作者 国家 所属机构（2025） 发文量 累计被引频数 Zuo Renguang China China University of Geosciences（中国地质大学(武汉)） 50 2 898 Carranza E. J. M. South Africa University of the Free State 17 1 897 Cheng Qiuming China China University of Geosciences（中国地质大学(北京)） 13 1 482 Singer D. A. United States The Pennsylvania State University 13 678 Pan Guocheng China China Jiliang University（中国计量大学） 12 203 Wang Gongwen China China University of Geosciences（中国地质大学(北京)） 12 294 Liu Yue China China University of Geosciences（中国地质大学(武汉)） 11 303 Parsa Mohammad Canada Geological Survey of Canada 11 301 Agterberg F. P. Canada Geological Survey of Canada 10 762 Maghsoudi Abbas Iran Amirkabir University of Technology 10 668
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表 3 高产研究机构发文量和被引频数统计
Table 3. Publications and citations of highly productive institutions
排名 研究机构 发文量 累计被引频数 1 China University of Geosciences（中国地质大学(武汉)） 106 5 030 2 United States Geological Survey 62 1 304 3 China University of Geosciences（中国地质大学(北京)） 59 1 401 4 Geological Survey of Canada 57 2 934 5 Central South University（中南大学） 24 357 6 University of KwaZulu-Natal 23 833 7 University of Twente 20 2 529 8 York University 20 1 973 9 University of the Free State 18 230 10 Amirkabir University of Technology 17 832
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表 4 矿产预测领域关键词分时期出现频数统计
Table 4. Frequencies of keywords in mineral prospectivity mapping counted by period
1969-1980年 1981-1990年 1991-2000年 2001-2010年 2011-2020年 2021-2025年 关键词 出现频数（次） 关键词 出现频数（次） 关键词 出现频数（次） 关键词 出现频数（次） 关键词 出现频数（次） 关键词 出现频数（次） mineral exploration 8 Kriging 7 mineral resource assessment 22 GIS 19 mineral prospectivity mapping 36 mineral prospectivity mapping 77 geochemistry 6 geochemistry 6 mineral exploration 14 mineral prospectivity mapping 18 geostatistics 18 machine learning 54 discriminant analysis 5 mineral exploration 5 GIS 9 weights of evidence 15 mineral exploration 16 deep learning 31 mining 5 geostatistics 5 data integration 7 artificial neural network 14 GIS 12 uncertainty 24 petroleum exploration 5 petroleum exploration 5 gold deposit 7 mineral resource assessment 10 gold deposit 12 mineral exploration 21 regression analysis 4 variogram 4 expert system 6 gold deposit 9 uncertainty 12 convolutional neural network 19 simulation 4 discriminant analysis 3 geostatistics 6 mineral exploration 8 machine learning 11 random forest 18 data processing 3 geophysical exploration 3 weights of evidence 6 fractal analysis 5 fractal analysis 10 gold deposit 17 Fortran 3 image analysis 3 classification 5 logistic regression 5 geochemical exploration 16 mineral resource assessment 14 geochemical exploration 3 multivariate analysis 3 Kriging 5 prospectivity 4 porphyry copper deposit 10 geostatistics 12 geostatistics 3 outliers 3 mineral prospectivity mapping 5 spatial association 4 Kriging 9 3D modeling 11 mineral resource assessment 3 prediction 3 knowledge-driven 4 uncertainty 4 random forest 9 geochemical data 11 trend analysis 3 resource appraisal 3 mineral deposit 4 conditional independence 3 weights of evidence 9 geochemical exploration 11 area of influence 2 trend analysis 3 petroleum exploration 4 fuzzy logic 3 logistic regression 8 support vector machine 9 classification 2 autocorrelation 2 variogram 4 fuzzy set 3 remote sensing 8 geophysical exploration 8 cluster analysis 2 cluster analysis 2 conditional simulation 3 geochemistry 3 3D modeling 7 GIS 7 conditional simulation 2 contouring 2 exploration target 3 geostatistics 3 artificial neural network 6 graph network 6 contouring 2 data integration 2 geochemistry 3 hydrothermal alteration 3 classification 6 porphyry copper deposit 6 deposit modeling 2 drilling patterns 2 interpolation 3 Kriging 3 compositional data analysis 6 remote sensing 6 graphics 2 exploration models 2 mineral deposit model 3 data integration 2 mineral resource assessment 6 Kriging 5
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表 5 被引频数前十的高被引论文信息
Table 5. Information of top 10 highly cited papers
论文信息 论文DOI 被引频数 Cressie, 1988, Math. Geol. 10.1007/BF00892986 466 Zuo and Carranza, 2011, Comput. & Geosci. 10.1016/j.cageo.2010.09.014 435 Cheng et al., 2000, Nat. Resour. Res. 10.1023/a:1010109829861 392 Filzmoser et al., 2005, Comput. & Geosci. 10.1016/j.cageo.2004.11.013 364 Carranza and Laborte, 2015, Comput. & Geosci. 10.1016/j.cageo.2014.10.004 320 Yousefi and Carranza, 2015, Comput. & Geosci. 10.1016/j.cageo.2015.03.007 308 Abedi et al., 2012, Comput. & Geosci. 10.1016/j.cageo.2011.12.014 282 Xiong and Zuo, 2016, Comput. & Geosci. 10.1016/j.cageo.2015.10.006 260 Cheng and Agterberg, 1999, Nat. Resour. Res. 10.1023/A:1021677510649 230 Porwal et al., 2003, Nat. Resour. Res. 10.1023/A:1022693220894 223
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Abedi, M., Norouzi, G. H., Bahroudi, A., 2012. Support Vector Machine for Multi-Classification of Mineral Prospectivity Areas. Computers & Geosciences, 46: 272-283. https://doi.org/10.1016/j.cageo.2011.12.014 Agterberg, F. P., 1974. Automatic Contouring of Geological Maps to Detect Target Areas for Mineral Exploration. Journal of the International Association for Mathematical Geology, 6(4): 373-395. https://doi.org/10.1007/BF02082358 Allais, M., 1957. Method of Appraising Economic Prospects of Mining Exploration over Large Territories: Algerian Sahara Case Study. Management Science, 3(4): 285-347. https://doi.org/10.1287/mnsc.3.4.285 Aria, M., Cuccurullo, C., 2017. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. Journal of Informetrics, 11(4): 959-975. https://doi.org/10.1016/j.joi.2017.08.007 Barry, G. S., Freyman, A. J., 1970. Mineral Endowment of the Canadian Northwest: A Subjective Probability Assessment. Canadian Mining and Metallurgical Bulletin, 63(701): 1031-1042. Carranza, E. J. M., Laborte, A. G., 2015. Random Forest Predictive Modeling of Mineral Prospectivity with Small Number of Prospects and Data with Missing Values in Abra (Philippines). Computers & Geosciences, 74: 60-70. https://doi.org/10.1016/j.cageo.2014.10.004 Chen, C. M., 2006. CiteSpace Ⅱ: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature. Journal of the American Society for Information Science and Technology, 57(3): 359-377. https://doi.org/10.1002/asi.20317 Cheng, Q. M., 2025. A New Paradigm for Mineral Resource Prediction Based on Human Intelligence-Artificial Intelligence Integration. Earth Science Frontiers, 32(4): 1-19 (in Chinese with English abstract). Cheng, Q. M., Agterberg, F. P., 1999. Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping. Natural Resources Research, 8(1): 27-35. https://doi.org/10.1023/A:1021677510649 Cheng, Q. M., Xu, Y. G., Grunsky, E., 2000. Integrated Spatial and Spectrum Method for Geochemical Anomaly Separation. Natural Resources Research, 9(1): 43-52. https://doi.org/10.1023/A:1010109829861 Cressie, N., 1988. Spatial Prediction and Ordinary Kriging. Mathematical Geology, 20(4): 405-421. https://doi.org/10.1007/BF00892986 Ellegaard, O., Wallin, J. A., 2015. The Bibliometric Analysis of Scholarly Production: How Great is the Impact? Scientometrics, 105(3): 1809-1831. https://doi.org/10.1007/s11192-015-1645-z Fang, Y. Y., 1987. Achievements of Computer Application in Our Institute during the "Sixth Five-Year Plan" Period. Earth Science, 12(3): 276-320, 256 (in Chinese with English abstract). Filzmoser, P., Garrett, R. G., Reimann, C., 2005. Multivariate Outlier Detection in Exploration Geochemistry. Computers & Geosciences, 31(5): 579-587. https://doi.org/10.1016/j.cageo.2004.11.013 Garfield, E., 2009. From the Science of Science to Scientometrics Visualizing the History of Science with HistCite Software. Journal of Informetrics, 3(3): 173-179. https://doi.org/10.1016/j.joi.2009.03.009 Gui, B. L., 1984. Principles for Estimating the Accuracy and Reliability of Mineral Resources Prediction and Assessment. Geological Review, 30(6): 536-543 (in Chinese with English abstract). Harris, D. P., 1973. A Subjective Probability Appraisal of Metal Endowment of Northern Sonora, Mexico. Economic Geology, 68(2): 222-242. https://doi.org/10.2113/gsecongeo.68.2.222 Hassan-Montero, Y., De-Moya-Anegón, F., Guerrero-Bote, V. P., 2022. SCImago Graphica: A New Tool for Exploring and Visually Communicating Data. El Profesional de la Información, : e310502. https://doi.org/10.3145/epi.2022.sep.02 Jing, L. H., Li, G. M., Ding, H. F., et al., 2025. Remote Sensing Prospecting, Geological Evaluation, and Significant Discovery of Porphyry Copper Deposits in the Middle-Western Segment of Bangonghu-Nujiang Metallogenic Belt, Tibet. Acta Petrologica Sinica, 41(2): 362-382 (in Chinese with English abstract). doi: 10.18654/1000-0569/2025.02.02 Kang, Q. L., Ding, Y. N., 2024. A Bibliometric Analysis of Global Research Trends of Exercise in Oncology during the Past Three Decades. Journal of Clinical and Nursing Research, 8(4): 219-224. https://doi.org/10.26689/jcnr.v8i4.6787 Mao, X. C., 2006. Research on 3D Digital Deposit and Stereo Quantitative Prediction of Concealed Ore Body (Dissertation). Central South University, Changsha (in Chinese with English abstract). Mao, X. C., Chen, G. G., 1988. The Xianghualing Sn-Deposit: Its Mathematical Model and Three-Dimensional Quantitative Prognostication. Geology and Prospecting, 24(10): 25-31 (in Chinese with English abstract). Mao, X. C., Duan, X., Deng, H., et al., 2026. Intelligent 3D Prediction of Deep Mineral Resources: Theory, Methods, and Challenges. Earth Science, 51(3): 793-815 (in Chinese with English abstract). Porwal, A., Carranza, E. J. M., Hale, M., 2003. Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping. Natural Resources Research, 12(1): 1-25. https://doi.org/10.1023/A:1022693220894 Price, D. J. D. S., 1963. Little Science, Big Science. Columbia University Press, New York. Pritchard, A., 1969. Statistical Bibliography or Bibliometrics. Journal of Documentation, 25: 348 Shi, L. Y., Zuo, R. G., 2026. Foundation Model for Mineral Prospectivity Mapping. Earth Science, 51(3): 832-848 (in Chinese with English abstract). Sun, T., Liu, Y., Bai, R., et al., 2025. Intelligent Mapping of Tungsten Prospectivity in Southern Jiangxi Province. Central South University Press, Changsha (in Chinese). Thiergärtner, H., 2006. Theory and Practice in Mathematical Geology—Introduction and Discussion. Mathematical Geology, 38(6): 659-665. https://doi.org/10.1007/s11004-006-9041-0 van Eck, N. J., Waltman, L., 2010. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics, 84(2): 523-538. https://doi.org/10.1007/s11192-009-0146-3 Wang, S. C., Cheng, Q. M., Fan, J. Z., 1989. Modelling of Gold Deposit Prospecting. Journal of Jilin University (Earth Science Edition), 19(3): 311-316 (in Chinese with English abstract). Wang, Y. Z., Wen, S. B., Li, B. W., et al., 2025. Construction Technology of Super-Agents for Intelligent Mineral Resources Prediction Driven by Large Model. Earth Science Frontiers, 32(4): 38-45 (in Chinese with English abstract). Xiao, K. Y., Li, C., Tang, R., et al., 2025. Big Data Intelligent Prediction and Evaluation. Earth Science Frontiers, 32(4): 20-37 (in Chinese with English abstract). Xiong, Y. H., Zuo, R. G., 2016. Recognition of Geochemical Anomalies Using a Deep Autoencoder Network. Computers & Geosciences, 86: 75-82. https://doi.org/10.1016/j.cageo.2015.10.006 Yousefi, M., Carranza, E. J. M., 2015. Prediction-Area (P-A) Plot and C-A Fractal Analysis to Classify and Evaluate Evidential Maps for Mineral Prospectivity Modeling. Computers & Geosciences, 79: 69-81. https://doi.org/10.1016/j.cageo.2015.03.007 Yu, C. W., 2003. The Complexity of Geological Systems. Geological Publishing House, Beijing (in Chinese). Yuan, F., Zhang, M. M., Li, X. H., et al., 2019. Prospectivity Modeling: From Twodimension to Three-Dimension. Acta Petrologica Sinica, 35(12): 3863-3874 (in Chinese with English abstract). doi: 10.18654/1000-0569/2019.12.18 Zhang, H. W., Sun, T., 2025. Bibliometric Analysis of Mineral Prospectivity Mapping Research from 2006 to 2024. Natural Resources Research, 1-27. https://doi.org/10.1007/s11053-025-10560-4 Zhang, X. Y., Zhang, Z. W., Zou, T. Y., et al., 2025. Research Status and Development Trend of Mine Pollution Control and Ecological Restoration Based on Bibliometrics Analysis. Environmental Pollution & Control, 47(10): 123-134 (in Chinese with English abstract). Zhao, P. D., Hu, W. L., Li, Z. J., 1983. Statistical Prediction of Ore Deposits. Geological Publishing House, Beijing (in Chinese). Zhou, Y. Z., Zuo, R. G., Liu, G., et al., 2021. The Great-Leap-Forward Development of Mathematical Geoscience during 2010-2019: Big Data and Artificial Intelligence Algorithm Are Changing Mathematical Geoscience. Bulletin of Mineralogy, Petrology and Geochemistry, 40(3): 556-573, 777 (in Chinese with English abstract). Zhu, Z. S., Zhang, Q. X., Yang, L. Q., 1990. Differentiation Theory and Model-Free Prediction. IWSPMR' 90, Wuhan (in Chinese). Zhu, Z. S., Zhu, L., 1998. Prediction Theory and Method System of Deposits. Journal of Chengdu University of Technology (Science & Technology Edition), 25(S1): 6-12 (in Chinese with English abstract). Zuo, R. G., 2025. Key Technology for Intelligent Mineral Prospectivity Mapping: Challenges and Solutions. Scientia Sinica (Terrae), 55(9): 3104-3119 (in Chinese with English abstract). doi: 10.1360/SSTe-2025-0096 Zuo, R. G., Carranza, E. J. M., 2011. Support Vector Machine: A Tool for Mapping Mineral Prospectivity. Computers & Geosciences, 37(12): 1967-1975. https://doi.org/10.1016/j.cageo.2010.09.014 Zuo, R. G., Carranza, E. J. M., 2023. Machine Learning-Based Mapping for Mineral Exploration. Mathematical Geosciences, 55(7): 891-895. https://doi.org/10.1007/s11004-023-10097-3 Zuo, R. G., Cheng, Q. M., Xu, Y., et al., 2024. Explainable Artificial Intelligence Models for Mineral Prospectivity Mapping. Scientia Sinica (Terrae), 54(9): 2917-2928 (in Chinese with English abstract). doi: 10.1360/N072024-0018 Zuo, R. G., Kreuzer, O. P., Wang, J., et al., 2021. Uncertainties in GIS-Based Mineral Prospectivity Mapping: Key Types, Potential Impacts and Possible Solutions. Natural Resources Research, 30(5): 3059-3079. https://doi.org/10.1007/s11053-021-09871-z 成秋明, 2025. 面向人类智能与人工智能融合的矿产资源预测新范式. 地学前缘, 32(4): 1-19. 方玉禹, 1987. 我院"六五"期间计算机应用成果. 地球科学, 12(3): 276-320, 256. 桂宝林, 1984. 矿产资源预测评价精确性与可靠性的估计原则. 地质论评, 30(6): 536-543. 荆林海, 李光明, 丁海峰, 等, 2025. 班怒带中西段斑岩铜矿遥感选区、地质评价与重大发现. 岩石学报, 41(2): 362-382. 毛先成, 2006. 三维数字矿床与隐伏矿体立体定量预测研究(博士学位论文). 长沙: 中南大学. 毛先成, 陈国珖, 1988. 香花岭锡矿床数学模型及立体定量预测初探. 地质与勘探, 24(10): 25-31. 毛先成, 段新明, 邓浩, 等, 2026. 深部矿产三维智能预测理论、方法与挑战. 地球科学, 51(3): 793-815. doi: 10.3799/dqkx.2025.227 师路易, 左仁广, 2026. 矿产预测大模型. 地球科学, 51(3): 832-848. doi: 10.3799/dqkx.2025.190 孙涛, 刘月, 白茹, 等, 2025. 赣南钨资源成矿潜力智能预测. 长沙: 中南大学出版社. 王世称, 成秋明, 范继璋, 1989. 金矿综合信息找矿模型. 长春地质学院学报, 19(3): 311-316. 王永志, 温世博, 李博文, 等, 2025. 大模型驱动的矿产资源智能预测超级智能体构建方法探索. 地学前缘, 32(4): 38-45. 肖克炎, 李程, 唐瑞, 等, 2025. 大数据智能预测评价. 地学前缘, 32(4): 20-37. 於崇文, 2003. 地质系统的复杂性. 北京: 地质出版社. 袁峰, 张明明, 李晓晖, 等, 2019. 成矿预测: 从二维到三维. 岩石学报, 35(12): 3863-3874. 张向阳, 张宗文, 邹天远, 等, 2025. 基于文献计量学的矿山污染治理和生态修复研究现状及发展趋势. 环境污染与防治, 47(10): 123-134. 赵鹏大, 胡旺亮, 李紫金, 1983. 矿床统计预测. 北京: 地质出版社. 周永章, 左仁广, 刘刚, 等, 2021. 数学地球科学跨越发展的十年: 大数据、人工智能算法正在改变地质学. 矿物岩石地球化学通报, 40(3): 556-573, 777. 朱章森, 张庆希, 杨丽清, 1990. 求异理论与无模型预测. 矿产资源统计预测国际学术讨论会, 武汉. 朱章森, 朱磊, 1998. 矿床定量预测理论与方法体系. 成都理工大学学报(自然科学版), 25(S1): 6-12. 左仁广, 2025. 智能矿产预测的技术挑战与解决方案. 中国科学: 地球科学, 55(9): 3104-3119. 左仁广, 成秋明, 许莹, 等, 2024. 可解释性矿产预测人工智能模型. 中国科学: 地球科学, 54(9): 2917-2928. -
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