Personal Profile

陈国雄，研究员（教授），博士生导师，1988年生，湖南怀化人。主要从事地质-地球物理-数学地球科学交叉研究，注重利用大数据、计算机模拟以及人工智能技术解决地学定量化问题；近年来聚焦极端地质事件（如超大陆聚散、大火成岩省、大氧化事件及其大规模成矿效应等）的定量表征、模拟及预测研究这一前沿领域，结合大数据与人工智能，取得创新成果如下：（1）建立了极端地质事件时空大数据挖掘的小波-分形-神经网络模型与方法体系；（2）为深部成矿弱缓信息表征和深部矿产资源定量预测提供了新的智能方法技术，被应于多个矿集区的找矿预测并取得成效；（3）为深时多种极端地质事件模拟预测提供了数据驱动模型与方法，揭示了超大陆聚散、沉积物俯冲以及大氧化事件协同演化新机制。全部成果在Nature Communications、Geology、Earth and Planetary Science Letters、Geophysical Research Letters、Journal of Geophysical Research: Solid Earth、Mathematical Geosciences、Geophysics、Computers & Geosciences等刊物上发表第一/通讯作者SCI论文23篇。应邀撰写英文《数学地球科学百科全书》长篇章节1篇。受邀担任Ore Geology Reviews副主编、Mathematical Geosciences期刊编委和客座主编、地球科学和Journal of Earth Science青年编委、智能地球物理专业委员会委员等。申请/获批国家发明专利4项和软件著作权2项。入选自然资源部高层次科技创新人才工程-青年科技人才（地质找矿方向）。

》招生方向

（1）数据驱动矿产资源预测与评价，要求：具有地质学/矿普/地球物理等学科背景

（2）数据驱动重建地球早期演化历史，要求：具有地质学/地球物理等学科背景

（3）地震数据智能解译与三维地质建模，要求：具有地球物理/GIS/计算机等学科背景

（4）背景噪声地震学，要求：具有地球物理学科背景

》科研项目

国家自然科学基金面上基金，基于多尺度小波-分形-神经网络的深部找矿信息挖掘研究，2020.1-2023.12，主持；

国家自然科学基金青年基金，小波域多重分形建模与深部矿致异常识别研究—以南岭矿集区为例，2018.1-2020.12，主持；

自然资源部中国地质调查局矿产地质调查项目，集宁覆盖区二维反射地震测量，2019.7-2020.5，主持；

自然资源部中国地质调查局矿产地质调查项目，集宁覆盖区背景噪声地震成像，2019.5-2020.4，主持；

鄂东南矿集区战略性矿产深部找矿理论与技术方法应用创新项目-委托业务，铜绿山矿田密集台阵面波层析成像技术研究，2022.6-2022.6，主持；

黔西南金矿多层次构造滑脱成矿系统研究与找矿预测项目-委托业务，黔西南板其-丫他金矿集区密集台阵地震面波层析成像研究，2023.6-2024.6，主持；

国家重点研发计划子课题，钴镍矿床找矿预测模型建立与找矿信息提取，2022.12-2026.12，研究骨干

国家重点研发计划“深部矿产资源预测理论与方法”项目子课题，“深部矿产三维预测模型与虚拟现实”，2016.7-2020.6，研究骨干。

》第一作者/通讯作者论文

Upcoming

34. Wang D., Chen G.*. Physics-guided Self-supervised Seismic Impedance Inversion. Submitted to Geophysics, 2024.

33. Chen J., Chen G.*, Wang D. SeisUNet:3D Seismic Fault Segmentation Using a Customized U-Net. Submitted to Geophysics, 2023.

32. Liang Q., Chen G.*, Luo L., Huang X., Hu H.. Appraising the Porphyry Cu Fertility Using Apatite Trace Elements: A Machine Learning Method. Submitted to Joural of Geochemical Exploration, 2023.

2024年

31. Luo L., Chen G.*, Identifying Tectonic Settings of Porphyry Copper Deposits Using Zircon Trace Elements – A Semisupervised Machine Learning Method. Ore Geology Reviews, 2024.

30. Li Q., Chen G.*, Wang D.. Mineral Prospectivity Mapping Using Semi-Supervised Machine learning. Mathematical Geosciences, 2024.

29. 敬嘉良，陈国雄*，程飞等，超短时线性台阵背景噪声成像技术在浅层地质结构探测中的应用，地球物理学进展，2023。

2023年及以前

28. Wang D., Chen G.*, Chen J.. Seismic Data Denoising Using a Self-Supervised Deep Learning Network. Mathematical Geosciences, 2023. https://doi.org/10.1007/s11004-023-10089-3. 

27. Chen G., Timothy Kusky, Luo L., Li Q., Cheng Q.*. Hadean Tectonics: Insights from Machine Learning. Geology, 2023, 51 (8): 718–722. 

26. Chen G.*, Cheng Q., Puetz Steve. Data-driven Discovery in Geosciences: Opportunities and Challenges. Mathematical Geosciences, 2023,  55 (3), 287-293. 

25. Li Q., Chen G.*, Luo L.. Mineral Prospectivity Mapping Using Attention–based Convolutional Neural Network. Ore Geology Reviews, 2023(156): 105381, https://doi.org/10.1016/j.oregeorev.2023.105381. 

24. 陈建玮，陈国雄*， 王德涛. 基于BiX-NAS的地震层序智能识别—以荷兰近海地区F3数据为例. 地球科学, 2023, doi: 10.3799/dqkx.2023.014. 

23. Wang D., Chen G.*. Intelligent Seismic Stratigraphic Modeling Using Temporal Convolutional Network, Computers & Geosciences, 2023 (171): 105294.

22. Chen G., Cheng Q.*, Timothy Lyons, Shen J., Frits Agterberg, Huang N., Zhao M.. Reconstructing Atmospheric Oxygenation History Using Machine Learning. 2022, Nature Communications, 13, 5862 (2022). https://doi.org/10.1038/s41467-022-33388-5.

21. Chen G.*, Huang N., Wu G., et al.. Mineral Prospectivity Mapping Based on Wavelet Neural Network and Monte Carlo Simulations: A Case Study from Nanling W-Sn Metallogenic Province. Ore Geology Reviews, 2022(143): 104765, https://doi.org/10.1016/j.oregeorev.2022.104765.

20. Luo L., Chen G.*, Xia Q. Tectonic-diffusion Estimates of Global Porphyry Molybdenum Resources. Nature Resources Research, 2022, 31, 751–766，https://doi.org/10.1007/s11053-022-10024-z.

19. Chen G. , Cheng Q.*, Shanan E. Peter, Christopher Spencer, Zhao M..Feedback between Surface and Deep Processes: Insight from Time Series Analysis of Sedimentary Record. Earth and Planetary Science Letters. 2022, 579: 117352. DOI: 10.1016/j.epsl.2021.117352.

18. 王德涛，陈国雄*. 基于时间卷积网络的地震波阻抗反演.地球科学,2022,47(04):1492-1506. 

17. Chen G.*, Zhang H., Wavelets in Geosciences. Earth Science Series. In: Daya Sagar B., Cheng Q., McKinley J., Agterberg F. (eds) Encyclopedia of Mathematical Geosciences. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_37-1.

16. Chen G.*, Cheng Q., Luo Y., Yang Y. Deng X. Seismic imaging of Caosiyao giant porphyry molybdenum deposit using ambient noise tomography. 2021，Geophysics, 86(6), B401-B412.

15. Wang D., Chen G.*, Seismic stratum segmentation using an encoder-decoder convolutional neural network. Mathematical Geosciences, 2021, 53: 1355-1374. 

14. Wu G., Chen G.*, Cheng Q., et al., Unsupervised Machine Learning for Lithological Mapping in Covered Areas, Jining, China, Using Geochemical Data. Nature Resources Research,  2020. 30 (2), 1053-1068. 

13. Yu H., Chen G.*, Gu H., A new multivariate pore-pressure prediction method based on machine learning, Computers & Geosciences. 2020(143): 104548. 

12. Wu G., Chen G.*, Wang D., et al. Identifying mineral prospectivity using seismic and potential field data in the Hongniangyu district, Inner Mongolia, China. Ore Geology Reviews. 2020(119):103317. 

11. Chen G., Cheng Q*. Cyclicity and Persistence of Earth’s Evolution Over Time Wavelet and Fractal Analysis. Geophysical Research Letters.  2018. 45: 8223-8230.

10. Chen G.*, Cheng Q. Fractal-Based Wavelet Filter for Separating Geophysical or Geochemical Anomalies from Background. Mathematical Geosciences, 2018, 50:249–272.

9. Chen G., Cheng Q.*, Fractal density modeling of crustal heterogeneity from the KTB deep hole, Journal of Geophysical Research: Solid Earth, 2017, 122:1919-1933.

8. Chen G., Cheng, Q.*, Zhang, H., Matched filtering method for separating magnetic anomaly using fractal model, Computers & Geosciences, 2016, 90: 179-188.

7. Chen G., Cheng, Q.*, Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration, Computers & Geosciences, 2016, 87: 56–66.

6. Chen G., Cheng, Q.*, Zuo, R., Fractal analysis of geochemical landscapes using scaling noise model, Journal of Geochemical Exploration, 2016, 161: 62-71.

5. Chen G., Liu,T., Sun, J., Cheng, Q.*, Sahoo, B., Zhang,Z., Zhang, H., Gravity method for investigating the geological structures associated with W–Sn polymetallic deposits in the Nanling Range, China, Journal of Applied Geophysics, 2015, 120: 14-25.

4. Chen G.*, Cheng, Q., Zuo, R., Liu, T., Xi, Y., Identifying gravity anomalies caused by granitic intrusions in Nanling mineral district, China: a multifractal perspective, Geophysical Prospecting, 2015, 63:256-270.

3. Chen G.*, Cheng, Q., Liu,T., Yang, Y., Mapping local singularities using magnetic data to investigate the volcanic rocks of the Qikou depression, Dagang oilfield, eastern China, Nonlinear Processes in Geophysics, 2013, 20: 501-511.

2. 陈国雄, 刘天佑*, 孙劲松, 欧洋, 刘双. 2014. 南岭花岗岩成矿带多尺度重力场及深部构造特征. 地球科学−中国地质大学学报, 39(2):240-250.

1. 陈国雄*, 孙劲松, 刘天佑. 2012.GRACE卫星时变重力场的小波多尺度分解：以2008年汶川Ms 8.0大震为例，武汉大学学报−信息科学版. 37(6):679-682.

》合作论文（部分）

Zhang, Z., Chen, G., Kusky, T., Yang, J., Cheng, Q.*, 2023. Lithospheric thickness records tectonic evolution by controlling metamorphic conditions. Science Advances 9 (50), eadi2134

Xu H, Luo Y, Yang Y, Shen W., Yin X., Chen G., et al. Three‐Dimensional Crustal Structures of the Shanxi Rift Constructed by Rayleigh Wave Dispersion Curves and Ellipticity: Implication for Sedimentation, Intraplate Volcanism, and Seismicity. Journal of Geophysical Research: Solid Earth, 2020, 125(11): e2020JB020146.

Zhang H, Ravat D, Marangoni Y R, Chen G., Hu X#. Improved total magnetization direction determination by correlation of the normalized source strength derivative and the RTP fields. Geophysics, 2018, 83(6): 1-45.

Zuo R., Wang J., Chen G., Yang M., 2015. Identification of weak anomalies: A multifractal perspective. Journal of Geochemical Exploration 154, 200-212.

Liu P., Liu T., Zhu P., Yang Y., Zhou Q., Zhang H., Chen G.. Depth Estimation for Magnetic and Gravity Anomaly Using Model Correction[J]. Pure and Applied Geophysics, 2017, 174(4): 1729-1742.

》会议摘要/报告

陈国雄，报告题目：深时数据驱动发现-沉积物俯冲及其资源环境效应，第二届数据驱动与地发展全国学术研讨会，2024.9，北京

Chen G., Drivers of Phanerozoic Marine Biodiversity: Insights from Machine Learning (Oral presentation). The 37th International Geological Congress (IGC), BEXCO, Busan, Korea, 2024.8

陈国雄，报告题目：数据驱动斑岩矿床成矿预测研究思路，第二十届全国数学地质与地学信息学术研讨会，2024.8，长春

陈国雄，报告题目：指针矿物大数据与机器学习成矿预测，第八届中国人工智能与大数据地球科学学术研讨会，2024.4，成都

陈国雄，报告题目：机器学习将今论古：优势与陷阱，高等学校地学虚拟仿真、人工智能和大数据与拔尖人才培养研讨会，2023.11，北京大学

Chen G., Machine Learning on Trace Elements Chemistry of Zircons Reveals Onset of Plate Tectonics since Hadean (Oral presentation). IAMG Annual Meeting, 2023, Trondheim, Norway.

陈国雄，报告题目：数据驱动地球科学与极端地质事件模拟预测，中国地质学会数据驱动与地发展专业委员会成立大会暨首届全国学术研讨会, 2023.4，珠海

陈国雄，报告题目：数据驱动机器学习重建地球大气氧化历史，固体地球科学重点实验室联盟学术年会, 2022.12，广州

陈国雄，报告题目：基于小波-分形-神经网络的找矿信息挖掘与矿产资源预测，全国首届金属矿产资源勘查大会，2021.10， 合肥

陈国雄，报告题目：金属矿床背景噪声成像技术，固体地球科学重点实验室联盟2021年实验技术与应用年会，2021.7， 北京

陈国雄，报告题目：地学大数据与智能找矿预测，鄂东南矿集区深部成矿规律与找矿预测研讨会，2021.6，大冶，湖北。