基于岩浆锆石微量元素的机器学习模型对斑岩铜矿成矿潜力的评估

吴川川; 孟旭阳; 刘品; 辛茹月; 成嘉伟

doi:10.3799/dqkx.2026.084

Volume 51 Issue 9

Sep. 2026

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2026 > 51(9): 3530-3546

Wu Chuanchuan, Meng Xuyang, Liu Pin, Xin Ruyue, Cheng Jiawei, 2026. Application of Machine Learning Models Based on Magmatic Zircon Trace Elements for Discriminating the Metallogenic Potential of Porphyry Copper Deposits. Earth Science, 51(9): 3530-3546. doi: 10.3799/dqkx.2026.084

Citation:

Wu Chuanchuan, Meng Xuyang, Liu Pin, Xin Ruyue, Cheng Jiawei, 2026. Application of Machine Learning Models Based on Magmatic Zircon Trace Elements for Discriminating the Metallogenic Potential of Porphyry Copper Deposits. Earth Science, 51(9): 3530-3546. doi: 10.3799/dqkx.2026.084

Citation:

Wu Chuanchuan, Meng Xuyang, Liu Pin, Xin Ruyue, Cheng Jiawei, 2026. Application of Machine Learning Models Based on Magmatic Zircon Trace Elements for Discriminating the Metallogenic Potential of Porphyry Copper Deposits. Earth Science, 51(9): 3530-3546. doi: 10.3799/dqkx.2026.084

PDF( 4477 KB)

Application of Machine Learning Models Based on Magmatic Zircon Trace Elements for Discriminating the Metallogenic Potential of Porphyry Copper Deposits

doi: 10.3799/dqkx.2026.084

Wu Chuanchuan^{1, 2
,
,},
Meng Xuyang^{1, 2
,
,},
Liu Pin^{3
,
,},
Xin Ruyue³,
Cheng Jiawei^{1, 2}

1.
Ministry of Natural Resources Key Laboratory for Exploration Theory and Technology of Critical Mineral Resources, School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
2.
State Key Laboratory of Geological Processes and Mineral Resources, School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
3.
School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing 100083, China

Received Date: 2025-12-15
Publish Date: 2026-09-25

Abstract

Abstract

To address the limitations of traditional binary discrimination diagrams, which arise from low dimensionality and difficulty in revealing nonlinear metallogenic mechanisms when processing zircon trace element data, this study introduces machine learning methods to enhance the accuracy and objectivity of porphyry copper mineralization potential assessment. Based on global zircon trace element data from porphyry deposits, we systematically compared the classification performance of five models-XGBoost, Random Forest, Support Vector Machine, Logistic Regression, and Neural Network- and interpreted the model decision-making mechanisms using the SHAP explainable framework. The results show that XGBoost achieves the highest classification accuracy (86.85%), followed by Random Forest (83.84%) and Logistic Regression (83.84%), while Support Vector Machine (78.90%) and Multilayer Perceptron (69.86%) exhibit relatively lower performance. SHAP analysis identifies Eu_N/Eu_N*, 10 000(Eu_N/Eu_N*)/Y, Ti, Y, △FMQ, and Gd/Dy as key indicators for discriminating mineralization potential. The models constructed in this study, such as XGBoost and Random Forest, can effectively identify porphyry copper mineralization potential and screen critical exploration indicators, offering not only new insights into intelligent mineral exploration but also important references for model selection and optimization in practical applications.
- porphyry copper deposit,
- machine learning,
- zircon trace elements,
- big data

FullText(HTML)

References(92)

References

Ballard, J. R., Palin, J. M., Campbell, I. H., 2002. Relative Oxidation States of Magmas Inferred from Ce(Ⅳ)/Ce(Ⅲ) in Zircon: Application to Porphyry Copper Deposits of Northern Chile. Contributions to Mineralogy and Petrology, 144(3): 347-364. https://doi.org/10.1007/s00410-002-0402-5

Barry, D. V., 2013. Decision Trees. Wiley Interdisciplinary Reviews: Computational Statistics, 5(6): 448-455. https://doi.org/10.1002/wics.1278

Bell, E. A., Boehnke, P., Harrison, T. M., 2016. Recovering the Primary Geochemistry of Jack Hills Zircons through Quantitative Estimates of Chemical Alteration. Geochimica et Cosmochimica Acta, 191: 187-202. https://doi.org/10.1016/j.gca.2016.07.016

Belousova, E., Griffin, W., O'Reilly, S. Y., et al., 2002. Igneous Zircon: Trace Element Composition as an Indicator of Source Rock Type. Contributions to Mineralogy and Petrology, 143(5): 602-622. https://doi.org/10.1007/s00410-002-0364-7

Boser, B. E., Guyon, I. M., Vapnik, V. N., 1992. A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory. Pittsburgh Pennsylvania USA. ACM, 144-152. https://doi.org/10.1145/130385.130401

Breiman, L., 2001. Random Forests. Machine Learning, 45(1): 5-32. https://doi.org/10.1023/A:1010933404324

Carrasco-Godoy, C., Campbell, I. H., Cajal, Y., 2024. Quantifying the Criteria Used to Identify Zircons from Ore-Bearing and Barren Systems in Porphyry Copper Exploration. Economic Geology, 119(5): 1035-1058. https://doi.org/10.5382/econgeo.5086

Chen, K., Zhu, Y., 2007. A Summary of Machine Learning and Related Algorithms. Journal of Statistics and Information, 22(5): 105-112 (in Chinese with English abstract).

Chen, T. Q., Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco. https://doi.org/10.1145/2939672.2939785

Chiaradia, M., 2014. Copper Enrichment in Arc Magmas Controlled by Overriding Plate Thickness. Nature Geoscience, 7(1): 43-46. https://doi.org/10.1038/ngeo2028

Dilles, J. H., Kent, A. J. R., Wooden, J. L., et al., 2015. Zircon Compositional Evidence for sulfur-Degassing from ore-Forming Arc Magmas. Economic Geology, 110(1): 241-251. https://doi.org/10.2113/econgeo.110.1.241

Guo, J., He, W. Y., 2024. Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain. Minerals, 14(9): 945. https://doi.org/10.3390/min14090945

He, K. M., Zhang, X. Y., Ren, S. Q., et al., 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). Santiago. https://doi.org/10.1109/iccv.2015.123

Hedenquist, J. W., Richards, J. P. 1998. The Influence of Geochemical Techniques on the Development of Genetic Models for Porphyry Copper Deposits. Reviews in Economic Geology, 235-256. https://doi.org/10.5382/Rev.10.10

Hezarkhani, A., 2006. Hydrothermal Evolution of the Sar-Cheshmeh Porphyry Cu-Mo Deposit, Iran: Evidence from Fluid Inclusions. Journal of Asian Earth Sciences, 28(4-6): 409-422. https://doi.org/10.1016/j.jseaes.2005.11.003

Hosmer, D. W. Jr, Lemeshow, S., Sturdivant, R. X., 2013. Applied Logistic Regression. Wiley, Hoboken. https://doi.org/10.1002/9781118548387

Hou, L. L., Wu, S., Yi, J. Z., et al., 2024. Discriminating Deposit Types Using Chlorite Trace Elements Based on Machine Learning. Earth Science, 49(12): 4303-4317 (in Chinese with English abstract).

Hou, Z. Q., Pan, X. F., Yang, Z. M., et al., 2007. Porphyry Cu-(Mo-Au) Deposits No Related to Oceanic-Slab Subduction: Examples from Chinese Porphyry Deposits in Continental Settings. Geoscience, 21: 332-351.

Hou, Z. Q., Zhang, H. R., Pan, X. F., et al., 2011. Porphyry Cu (-Mo-Au) Deposits Related to Melting of Thickened Mafic Lower Crust: Examples from the Eastern Tethyan Metallogenic Domain. Ore Geology Reviews, 39(1-2): 21-45. https://doi.org/10.1016/j.oregeorev.2010.09.002

Houston, R. A., Dilles, J. H., 2013. Structural Geologic Evolution of the Butte District, Montana. Economic Geology, 108(6): 1397-1424. https://doi.org/10.2113/econgeo.108.6.1397

Jiang, N., Yang, H. Y., Gu, Q. C., et al., 2019. Machine Learning and Its Algorithm and Development Analysis. China Computer & Communication, 31(1): 83-84, 87 (in Chinese with English abstract).

Lee, R. G., Plouffe, A., Ferbey, T., et al., 2021. Recognizing Porphyry Copper Potential from till Zircon Composition: A Case Study from the Highland Valley Porphyry District, south-Central British Columbia. Economic Geology, 116(4): 1035-1045. https://doi.org/10.5382/econgeo.4808

Lee, T. H., Ullah, A., Wang, R., 2019. Bootstrap Aggregating and Random Forest. Macroeconomic Forecasting in the Era of Big Data. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-31150-6_13

Li, C. B., Xiao, K. Y., Li, N., et al., 2020. A Comparative Study of Support Vector Machine, Random Forest and Artificial Neural Network Machine Learning Algorithms inGeochemical Anomaly Information Extraction. Acta Geoscientia Sinica, 41(2): 309-319 (in Chinese with English abstract).

Li, H. X., Gao, M. Q., Ji, X. H., et al., 2025. Machine Learning-Based Tectonic Discrimination Using Basalt Element Geochemical Data: Insights into the Carboniferous-Permian Tectonic Regime of Western Tianshan Orogen. Minerals, 15(2): 122. https://doi.org/10.3390/min15020122

Liu, J. Q., Zhong, S. H., Li, S. Z., et al., 2023. Identification of Mineralized and Barren Magmatic Rocks for the Pophryry-Skarn Deposits from the Qimantagh, East Kunlun: Based on Machine Learning and Whole-Rock Compositions. Northwestern Geology, 56(6): 41-56 (in Chinese with English abstract).

Liu, X. L., Li, C, Wang, T. S., 2022. An Improved Support Vector Machine Model for Personal Credit Assessment. Computer Science and Technology, 1(1): 27-34 (in Chinese with English abstract).

Liu, X. S., Zhang, Z. B., 2019. Parameter Optimization of Support Vector Machine Based on Improved Grid Search Method. Journal of Jiangxi University of Science and Technology, 40(1): 5-9 (in Chinese with English abstract).

Loader, M. A., Nathwani, C. L., Wilkinson, J. J., et al., 2022. Controls on the Magnitude of Ce Anomalies in Zircon. Geochimica et Cosmochimica Acta, 328: 242-257. https://doi.org/10.1016/j.gca.2022.03.024

Loader, M. A., Wilkinson, J. J., Armstrong, R. N., 2017. The Effect of Titanite Crystallisation on Eu and Ce Anomalies in Zircon and Its Implications for the Assessment of Porphyry Cu Deposit Fertility. Earth and Planetary Science Letters, 472: 107-119. https://doi.org/10.1016/j.epsl.2017.05.010

Loucks, R. R., Fiorentini, M. L., 2023. Oxidation of Magmas during Gain and Loss of H₂O Recorded by Trace Elements in Zircon. Earth and Planetary Science Letters, 622: 118377. https://doi.org/10.1016/j.epsl.2023.118377

Loucks, R. R., Fiorentini, M. L., Henríquez, G. J., 2020. New Magmatic Oxybarometer Using Trace Elements in Zircon. Journal of Petrology, 61(3): egaa034. https://doi.org/10.1093/petrology/egaa034

Lu, Y. J., Loucks, R. R., Fiorentini, M., et al., 2016. Zircon Compositions as a Pathfinder for Porphyry Cu±Mo±Au Deposits. Society of Economic Geologists. 329-347. https://doi.org/10.5382/sp.19.13

Luo, L., Chen, G. X., Li, Z. H., 2024. Identifying Tectonic Settings of Porphyry Copper Deposits Using Zircon Trace Elements-a Semi-Supervised Machine Learning Method. Ore Geology Reviews, 171: 106170. https://doi.org/10.1016/j.oregeorev.2024.106170

Meng, X. Y., Kleinsasser, J. M., Richards, J. P., et al., 2021. Oxidized Sulfur-Rich Arc Magmas Formed Porphyry Cu Deposits by 1.88 Ga. Nature Communications, 12: 2189. https://doi.org/10.1038/s41467-021-22349-z

Meng, X. Y., Mao, J. W., 2025. Temporal Distribution of Porphyry Copper Deposits and the Influencing Factors. Acta Petrologica Sinica, 41(12): 4164-4177 (in Chinese with English abstract). doi: 10.18654/1000-0569/2025.12.13

Meng, X. Y., Mao, J. W., Simon, A., et al., 2024. Contrasting Tectonomagmatic Conditions for Coexisting Iron Oxide-Apatite Deposits and Porphyry and Skarn Cu±Au Deposits in the Middle-Lower Yangtze River Metallogenic Belt, China. Economic Geology, 119(5): 1059-1087. https://doi.org/10.5382/econgeo.5084

Meng, X. Y., Simon, A. C., Kleinsasser, J. M., et al., 2022. Formation of Oxidized Sulfur-Rich Magmas in Neoarchaean Subduction Zones. Nature Geoscience, 15(12): 1064-1070. https://doi.org/10.1038/s41561-022-01071-5

Mungall, J. E., 2002. Roasting the Mantle: Slab Melting and the Genesis of Major Au and Au-Rich Cu Deposits. Geology, 30(10): 915. https://doi.org/10.1130/0091-7613(2002)0300915:rtmsma>2.0.co;2 doi: 10.1130/0091-7613(2002)0300915:rtmsma>2.0.co;2

Nathwani, C. L., Wilkinson, J. J., Fry, G., et al., 2022. Machine Learning for Geochemical Exploration: Classifying Metallogenic Fertility in Arc Magmas and Insights into Porphyry Copper Deposit Formation. Mineralium Deposita, 57(7): 1143-1166. https://doi.org/10.1007/s00126-021-01086-9

Nathwani, C., Blundy, J., Large, S. J. E., et al., 2024. A Zircon Case for Super-Wet Arc Magmas. Nature Communications, 15: 8982. https://doi.org/10.1038/s41467-024-52786-5

Pan, B. D., Li, B., Wang, F. C., et al., 2022. Quantitative Study on Spatial Correlation of Different Types of Deposits (Occurrences): A Case Study of Indochina Peninsula and Adjacent Areas. Geology and Exploration, 58(3): 532-544 (in Chinese with English abstract).

Pedregosa, F., Varoquaux, G., Gramfort, A., et al., 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. https://doi.org/10.48550/arXiv.1201.0490

Petrelli, M., Perugini, D., 2016. Solving Petrological Problems through Machine Learning: The Study Case of Tectonic Discrimination Using Geochemical and Isotopic Data. Contributions to Mineralogy and Petrology, 171(10): 81. https://doi.org/10.1007/s00410-016-1292-2

Piquer, J., Sanchez-Alfaro, P., Pérez-Flores, P., 2021. A New Model for the Optimal Structural Context for Giant Porphyry Copper Deposit Formation. Geology, 49(5): 597-601. https://doi.org/10.1130/g48287.1

Pizarro, H., Campos, E., Bouzari, F., et al., 2020. Porphyry Indicator Zircons (PIZs): Application to Exploration of Porphyry Copper Deposits. Ore Geology Reviews, 126: 103771. https://doi.org/10.1016/j.oregeorev.2020.103771

Pizarro, H., Rousse, S., Bouzari, F., et al., 2024. Use of Porphyry Indicator Zircons (PIZs) in the Sedimentary Record as an Exploration Tool for Covered Porphyry Copper Deposits in the Atacama Desert, Chile. Journal of Geochemical Exploration, 256: 107351. https://doi.org/10.1016/j.gexplo.2023.107351

Reunanen, J. 2003. Overfitting in Making Comparisons Between Variable Selection Methods. Journal of Machine Learning Research, 3(3): 1371-1382.

Richards, J. P., 2003. Tectono-Magmatic Precursors for Porphyry Cu-(Mo-Au) Deposit Formation. Economic Geology, 98(8): 1515-1533. https://doi.org/10.2113/gsecongeo.98.8.1515

Richards, J. P., Kerrich, R., 2007. Special Paper: Adakite-Like Rocks: Their Diverse Origins and Questionable Role in Metallogenesis. Economic Geology, 102(4): 537-576. https://doi.org/10.2113/gsecongeo.102.4.537

Rudin, C., 2019. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence, 1(5): 206-215. https://doi.org/10.1038/s42256-019-0048-x

Sahli, H. 2020. An Introduction to Machine Learning. In: Laffly, D., ed., TORUS 1-Toward an Open Resource Using Services : Cloud Computing for Environmental Data. Wiley, Hoboken. https://doi.org/10.1002/9781119720492.ch7

Shen, P., Hattori, K., Pan, H. D., et al., 2015. Oxidation Condition and Metal Fertility of Granitic Magmas: Zircon Trace-Element Data from Porphyry Cu Deposits in the Central Asian Orogenic Belt. Economic Geology, 110(7): 1861-1878. https://doi.org/10.2113/econgeo.110.7.1861

Sillitoe, R. H., 1972. A Plate Tectonic Model for the Origin of Porphyry Copper Deposits. Economic Geology, 67(2): 184-197. https://doi.org/10.2113/gsecongeo.67.2.184

Sillitoe, R. H., 1993. Gold-Rich Porphyry Copper Deposits: Geological Model and Exploration Implication. Geological Association of Canada-Special Paper, 40: 465-478.

Sillitoe, R. H., 2010. Porphyry Copper Systems. Economic Geology, 105(1): 3-41. https://doi.org/10.2113/gsecongeo.105.1.3

Song, Y. Y., Lu, Y., 2015. Decision Tree Methods: Applications for Classification and Prediction. Shanghai Archives of Psychiatry, 27: 130-135. https://doi.org/10.11919/j.issn.1002-0829.215044

Stonadge, G., Miles, A., Smith, D., et al., 2023. The Volatile Record of Volcanic Apatite and Its Implications for the Formation of Porphyry Copper Deposits. Geology, 51(12): 1158-1162. https://doi.org/10.1130/g51461.1

Sun, H. L., Tang, R., Li, Y., et al., 2026. Three-Dimensional Mineral Prospectivity Prediction Based on Mineralization Process Numerical Simulation and Machine Learning: A Case Study of the Maodeng Deposit in Inner Mongolia. Earth Science51(3): 921-939 (in Chinese with English abstract).

Sun, W. D., Huang, R. F., Li, H., et al., 2015. Porphyry Deposits and Oxidized Magmas. Ore Geology Reviews, 65: 97-131. https://doi.org/10.1016/j.oregeorev.2014.09.004

Teng, W. H., Pan, Z. F., Wang, R., 2025. Machine-Learning-Based Classification of Igneous Rock Type Using Titanite Trace Element Data. Journal of Chengdu University of Technology (Science and Technology Edition), 52(5): 873-887 (in Chinese with English abstract).

Trail, D., Watson, E. B., Tailby, N. D., 2012. Ce and Eu Anomalies in Zircon as Proxies for the Oxidation State of Magmas. Geochimica et Cosmochimica Acta, 97: 70-87. https://doi.org/10.1016/j.gca.2012.08.032

Wang, L. Y., Zhang, C., Geng, R., et al., 2023a. The Discrimination of Tectonic Settings Using Trace Elements in Magmatic Zircons: A Machine Learning Approach. Earth Science Informatics, 16(4): 4097-4112. https://doi.org/10.1007/s12145-023-01142-0

Wang, Y. Z., Hasanipanah, M., Rashid, A. S. A., et al., 2023b. Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests. Materials, 16(10): 3731. https://doi.org/10.3390/ma16103731

Wang, Y., Qiu, K. F., Hou, Z. L., et al., 2022. Quartz Ti/Ge-P Discrimination Diagram: A Machine Learning Based Approach for Deposit Classification. Acta Petrologica Sinica, 38(1): 281-290 (in Chinese with English abstract). doi: 10.18654/1000-0569/2022.01.18

Watson, E. B., Harrison, T. M., 2005. Zircon Thermometer Reveals Minimum Melting Conditions on Earliest Earth. Science, 308(5723): 841-844. https://doi.org/10.1126/science.1110873

Wen, Z. H., Li, L., Kirkland, C. L., et al., 2024a. A Machine Learning Approach to Discrimination of Igneous Rocks and Ore Deposits by Zircon Trace Elements. American Mineralogist, 109(6): 1129-1142. https://doi.org/10.2138/am-2022-8899

Wen, Z. H., Xu, B., Kirkland, C. L., et al., 2024b. A Machine Learning Zircon Trace Element Tool to Predict Porphyry Deposit Type and Resource Size. Journal of Geophysical Research: Machine Learning and Computation, 1(4): e2024JH000180. https://doi.org/10.1029/2024JH000180

Yang, J. F., Qiao, P. R., Li, Y. M., et al., 2019. A Review of Machine-Learning Classification and Algorithms. Statistics and Decision, 35(6): 36-40 (in Chinese with English abstract).

Yang, S. Q., Yang, W., Cui, T., et al., 2024. Prediction and Practical Application of Bauxite Mineralization in Wuzhengdao Area, Guizhou, China. PLoS One, 19(7): e0305917. https://doi.org/10.1371/journal.pone.0305917

Yuan, L. L., Chai, P., Hou, Z. Q., et al., 2025. Machine Learning for Characterizing Magma Fertility in Porphyry Copper Deposits: A Case Study of Southeastern Tibet. Acta Geologica Sinica (English Edition), 99(2): 611-624. https://doi.org/10.1111/1755-6724.15277

Zhang, R., Wang, Y. B., 2016. Research on Machine Learning with Algorithm and Development. Journal of Communication University of China Science and Technology, 23(2): 10-18, 24 (in Chinese with English abstract).

Zhao, W. G., Chen, L., Chen, J. L., 2021. Application of Machine Learning Methods in Mineral Exploration. World Nonferrous Metals, (1): 1-5 (in Chinese with English abstract).

Zhong, R. C., Deng, Y., Yu, C., 2021. Multi-Layer Perceptron-Based Tectonic Discrimination of Basaltic Rocks and an Application on the Paleoproterozoic Xiong'er Volcanic Province in the North China Craton. Computers & Geosciences, 149: 104717. https://doi.org/10.1016/j.cageo.2021.104717

Zhong, S. H., Liu, Y., Li, S. Z., et al., 2023. A Machine Learning Method for Distinguishing Detrital Zircon Provenance. Contributions to Mineralogy and Petrology, 178(6): 35. https://doi.org/10.1007/s00410-023-02017-9

Zhou, Y. Z., Zhang, Z. J., Yang, J., et al., 2022. Machine Learning and Singularity Analysis Reveal Zircon Fertility and Magmatic Intensity: Implications for Porphyry Copper Potential. Natural Resources Research, 31(6): 3061-3078. https://doi.org/10.1007/s11053-022-10122-y

Zou, S. H., Chen, X. L., Brzozowski, M. J., et al., 2022. Application of Machine Learning to Characterizing Magma Fertility in Porphyry Cu Deposits. Journal of Geophysical Research: Solid Earth, 127(8): e2022JB024584. https://doi.org/10.1029/2022JB024584

陈凯, 朱钰, 2007. 机器学习及其相关算法综述. 统计与信息论坛, 22(5): 105-112.

侯霖莉, 吴松, 易建洲, 等, 2024. 基于机器学习的绿泥石微量元素判别矿床类型. 地球科学, 49(12): 4303-4317. doi: 10.3799/dqkx.2023.173

姜娜, 杨海燕, 顾庆传, 等, 2019. 机器学习及其算法和发展分析. 信息与电脑, 31(1): 83-84, 87.

李苍柏, 肖克炎, 李楠, 等, 2020. 支持向量机、随机森林和人工神经网络机器学习算法在地球化学异常信息提取中的对比研究. 地球学报, 41(2): 309-319.

刘嘉情, 钟世华, 李三忠, 等, 2023. 基于机器学习和全岩成分识别东昆仑祁漫塔格斑岩‒矽卡岩矿床成矿岩体和贫矿岩体. 西北地质, 56(6): 41-56.

刘晓丽, 李程, 王天珊, 2022. 改进的支持向量机的个人信用评估模型. 计算机科学与技术, 1(1): 27-34.

刘小生, 章治邦, 2019. 基于改进网格搜索法的SVM参数优化. 江西理工大学学报, 40(1): 5-9.

孟旭阳, 毛景文, 2025. 斑岩铜矿时间分布规律与影响因素. 岩石学报, 41(12): 4164-4177.

潘北斗, 李宝, 王福臣, 等, 2022. 不同类型矿床(点)空间关联关系定量研究‒以中南半岛及临近地区为例. 地质与勘探, 58(3): 532-544

孙会玲, 唐瑞, 李杨, 等, 2026. 基于成矿过程数值模拟与机器学习的三维找矿预测: 以内蒙古毛登矿床为例. 地球科学, 51(3): 921-939. doi: 10.3799/dqkx.2026.012

滕文航, 潘增锋, 王瑞, 2025. 基于机器学习的榍石微量元素特征判别岩浆岩类型. 成都理工大学学报(自然科学版), 52(5): 873-887.

王瑀, 邱昆峰, 侯照亮, 等, 2022. 石英Ti/Ge-P: 基于机器学习的矿床类型判别新图解. 岩石学报, 38(1): 281-290.