Clustering of Geological Characteristics and Prospecting Significance of Gold Deposits in the Jiangnan Orogen Based on Knowledge Graphs
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摘要:
为深化对江南造山带金矿成矿规律的理解，进一步评估找矿潜力.本文以江南造山带及其邻区的金矿为研究对象，引入知识图谱技术，采用自上而下方法构建金矿领域知识模型，并综合利用深度学习与大语言模型构建金矿成矿‒勘查知识图谱；基于该知识图谱开展金矿社区聚类分析与Jaccard相似性评价，系统分析矿床聚集性特征.构建了含28类实体类型、10种语义关系类型的金矿领域知识模型，由此生成的知识图谱涵盖区域内60个代表性矿床，包含2 212条实体及5 497条语义关系.社区聚类分析成功提取了“蚀变‒矿物‒地层”等关键控矿要素组合及成矿规律；Jaccard系数分析显示，水口山、黄金洞金矿与世界大型‒超大型矿床具有高度相似性，揭示出两矿床具有巨大的深部及外围找矿潜力.
Abstract:This study aims to deepen the understanding of metallogenic regularities and evaluate the prospecting potential of gold deposits in the Jiangnan Orogen. Focusing on the gold deposits within and adjacent to the Jiangnan Orogen, technologies related to the knowledge graph were introduced. A domain knowledge schema was developed using a top-down approach, and the metallogeny-exploration knowledge graph of gold deposits was constructed by integrating deep learning and Large Language Models (LLM). Community detection and Jaccard similarity evaluation were used to analyze the clustering characteristics of the gold deposits. The knowledge schema contains 28 geological entity types and 10 semantic relationship types. The resulting knowledge graph encompasses 60 representative gold deposits in the region, containing 2 212 geological entities and 5 497 semantic relationships. Community detection successfully extracted key ore-controlling factor combinations and metallogenic regularities, such as "alteration-mineral-strata". Jaccard similarity analysis indicates that the Shuikoushan and Huangjindong gold deposits have high similarities to global large-to-giant deposits, revealing significant prospecting potential in their deep-seated zones and peripheral areas.
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图 1 金矿成矿‒勘查领域本体模型
Fig. 1. Ontology model in the field of gold mineralization and exploration
图 2 江南造山带及邻区金矿部分知识图谱
Fig. 2. Partial knowledge graph of gold mines in the Jiangnan orogenic belt and adjacent areas
图 3 江南造山带及邻区金矿知识图谱中实体统计分布
Fig. 3. Entity statistics distribution in the knowledge graph of gold deposits in the Jiangnan orogenic belt and adjacent areas
图 4 江南造山带及邻区金矿知识图谱中语义关系统计分布
Fig. 4. Semantic relation statistics distribution in the knowledge graph of gold deposits in Jiangnan orogenic belt and adjacent areas
图 5 模块度、聚类数随解析度变化趋势图
Fig. 5. Trends of modularity and number of clusters changing with resolution
图 6 江南造山带及邻区金矿矿床实体标签聚类节点分布
Fig. 6. Distribution of entity label clustering nodes of gold deposits in Jiangnan orogenic belt and adjacent areas
图 7 不同类别中存在的矿床及地质要素
Fig. 7. Gold deposits and their geological characteristics present in different categories
图 8 江南造山带及邻区金矿知识图谱核心关联特征（K=3）
Fig. 8. Core correlation features of the knowledge graph of gold deposits in the Jiangnan orogenic belt and adjacent areas (K=3)
图 9 水口山矿床不同社区中存在的地质要素
Fig. 9. Geological elements existing in different communities in Shuikoushan deposit
图 10 黄金洞矿床不同社区中存在的地质要素
Fig. 10. Geological elements existing in different communities in Huangjindong deposit
图 11 水口山矿床与黄金洞矿床与大型‒超大型矿床地质要素Jaccard相似性热力图
Fig. 11. Jaccard similarity heatmap of geological elements between the Shuikoushan deposit, Huangjindong deposit and large- super large deposits
图 12 水口山矿床和黄金洞矿床与大型‒超大型矿床地质要素Jaccard相似性平均值
Fig. 12. Average Jaccard similarity values of geological elements in Shuikoushan deposit and Huangjindong deposit with large-super large deposits
图 13 水口山矿床与大型‒超大型矿床的综合相似性对比
Fig. 13. Comprehensive similarity comparison between Shuikoushan deposit and large - super large deposits
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Bai, D. Y., Li, B., Zeng, G. Q., et al., 2025. Tectonic Ore-Controlling Characteristics and Dynamic Mechanism of the Hunan Segment of the Jiangnan Gold Ore Belt. Geotectonica et Metallogenia, Online (in Chinese with English abstract). https://link.cnki.net/urlid/44.1595.P.20251222.1628.002 Blondel, V. D., Guillaume, J., Lambiotte, R., et al., 2008. Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10): P10008. https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008 doi: 10.1088/1742-5468/2008/10/P10008 Deng, Y. Y., Song, S. C., Fan, J. X., et al., 2024. Paleontology Knowledge Graph for Data-Driven Discovery. Journal of Earth Science, 35(3): 1024-1034. https://doi.org/10.1007/s12583-023-1943-9 Dong, S. C., Shi, Y. K., Ran, Y. Z., et al., 2024. Biological Classification System Knowledge Graph and Semi-Automatic Construction of Its Invertebrate Fossil Branches. Journal of Earth Science, 35(6): 2119-2128. https://doi.org/10.1007/s12583-023-1941-y Enkhsaikhan, M., Liu, W., Holden, E. J., et al., 2021. Auto-Labelling Entities in Low-Resource Text: A Geological Case Study. Knowledge and Information Systems, 63(3): 695-715. https://doi.org/10.1007/s10115-020-01532-6 Guo, F., Lai, P., Huang, F. M., et al., 2024. Literature Review and Research Progress of Landslide Susceptibility Mapping Based on Knowledge Graph. Earth Science, 49(5): 1584-1606 (in Chinese with English abstract). Kong, Y. X., Shi, G. Y., Wu, R. J., et al., 2019. K-Core: Theories and Applications. Physics Reports, 832: 1-32. https://doi.org/10.1016/j.physrep.2019.10.004 Li, G. Z., Wang, P., Liu, J. J., et al., 2024. Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors. arXiv, 2404.17807. https://arxiv.org/abs/2404.17807 Li, J., Huang, X. J., Gao, Y. T., et al., 2022. Distant Supervised Relation Extraction Based on Sentence-Level Attention with Relation Alignment. Artificial Intelligence and Security. Cham: Springer International Publishing: 142-152. https://doi.org/10.1007/978-3-031-06794-5_12 Li, S., Chen, J. P., Xiang, J., 2018. Prospecting Information Extraction by Text Mining Based on Convolutional Neural Networks: A Case Study of the Lala Copper Deposit, China. IEEE Access, 6: 52286-52297. https://ieeexplore.ieee.org/document/8466566 Ma, X. F., 2025. Research on Knowledge Extraction Method of Geological Hazards Based on Large Models (Dissertation). Qinghai Normal University, Xining (in Chinese with English abstract). Ma, X. G., Ma, C., Wang, C. B., 2020. A New Structure for Representing and Tracking Version Information in a Deep Time Knowledge Graph. Computers & Geosciences, 145: 104620. https://doi.org/10.1016/j.cageo.2020.104620 Newman, M. E. J., 2006. Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23): 8577-8582. https://doi.org/10.1073/pnas.0601602103 Niwattanakul, S., Singthongchai, J., Naenudorn, E., et al., 2013. Using of Jaccard Coefficient for Keywords Similarity. The International Multiconference of Engineers and Computer Scientists, Hong Kong. Peng, C. Y., Xia, F., Naseriparsa, M., et al., 2023. Knowledge Graphs: Opportunities and Challenges. Artificial Intelligence Review, 56(11): 13071-13102. https://doi.org/10.1007/s10462-023-10465-9 Qiu, Q. J., Ma, K., Lü, H. R., et al., 2023a. Construction and Application of a Knowledge Graph for Iron Deposits Using Text Mining Analytics and a Deep Learning Algorithm. Mathematical Geosciences, 55(3): 423-456. https://doi.org/10.1007/s11004-023-10050-4 Qiu, Q. J., Tian, M., Xie, Z., et al., 2023b. Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach. Journal of Earth Science, 34(5): 1406-1417. https://doi.org/10.1007/s12583-022-1789-8 Qiu, Q. J., Wu, L., Ma, K., et al., 2023. A Knowledge Graph Construction Method for Geohazard Chain for Disaster Emergency Response. Earth Science, 48(5): 1875-1891 (in Chinese with English abstract). Qiu, Q. J., Xie, Z., Wu, L., et al., 2020. Dictionary-Based Automated Information Extraction from Geological Documents Using a Deep Learning Algorithm. Earth and Space Science, 7(3): e2019EA000993. https://doi.org/10.1029/2019ea000993 Tian, M., Ma, K., Wu, Q. R., et al., 2024. Joint Extraction of Entity Relations from Geological Reports Based on a Novel Relation Graph Convolutional Network. Computers & Geosciences, 187: 105571. https://doi.org/10.1016/j.cageo.2024.105571 Vaswani, A., Shazeer, N., Parmar, N., et al., 2017. Attention is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach. https://doi.org/10.5555/3295222.3295349 Wang, B., Wu, L., Xie, Z., et al., 2022. Understanding Geological Reports Based on Knowledge Graphs Using a Deep Learning Approach. Computers & Geosciences, 168: 105229. https://doi.org/10.1016/j.cageo.2022.105229 Wang, C. B., Li, Y. J., Chen, J. G., 2023. Text Mining and Knowledge Graph Construction from Geoscience Literature Legacy: A Review. In: Ma, X., Mookerjee, M., Hsu, L., et al., eds., Recent Advancement in Geoinformatics and Data Science. Geological Society of America, New York. https://doi.org/10.1130/2022.2558(02) Wang, C. B., Ma, X. G., Chen, J. G., et al., 2018. Information Extraction and Knowledge Graph Construction from Geoscience Literature. Computers & Geosciences, 112: 112-120. https://doi.org/10.1016/j.cageo.2017.12.007 Wang, J. X., 2024. Intelligent Identification of Prospecting Criteria for Porphyry Copper Deposits Based on Large Language Models (Dissertation). Jilin University, Changchun (in Chinese with English abstract). Wei, Z. P., Su, J. L., Wang, Y., et al., 2020. A Novel Cascade Binary Tagging Framework for Relational Triple Extraction. The 58th Annual Meeting of the Association for Computational Linguistics. Online. 10.18653/v1/2020.acl-main.136 Wu, R. Z., Li, H., Mei, H. B., et al., 2025. A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph. Earth Science, 50(10): 4125-4136 (in Chinese with English abstract). Xiao, D., Song, W. G., Yan, Z. F., et al., 2025. Geological Characteristics, Metallogenic Regularity and Metallogenic Model of Gold Deposits in the Xuefeng Arcuate Tectonic Belt of Hunan Province. Geology and Exploration, 61(3): 450-463 (in Chinese with English abstract). Yang, X., Sun, L., Liu, M. L., et al., 2025. Knowledge Graph Construction with BERT-BiLSTM-IDCNN-CRF and Graph Algorithms for Metallogenic Pattern Discovery: A Case Study of Pegmatite-Type Lithium Deposits in China. Ore Geology Reviews, 179: 106514. https://doi.org/10.1016/j.oregeorev.2025.106514 Zhang, L. N., Hou, Z. S., Shen, B. H., et al., 2023a. Paleobiogeographic Knowledge Graph: An Ongoing Work with Fundamental Support for Future Research. Journal of Earth Science, 34(5): 1339-1349. https://doi.org/10.1007/s12583-023-1845-z Zhang, L., Hou, M. C., Chen, A. Q., et al., 2023b. Construction of a Fluvial Facies Knowledge Graph and Its Application in Sedimentary Facies Identification. Geoscience Frontiers, 14(2): 101521. https://doi.org/10.1016/j.gsf.2022.101521 Zhou, C. H., Wang, H., Wang, C. S., et al. 2021. Research on Geoscience Knowledge Graph in the Big Data Era. Scientia Sinica Terrae, 51(7): 1070-1079 (in Chinese with English abstract). doi: 10.1360/SSTe-2020-0337 Zhou, C. H., Wang, H., Wang, C. S., et al., 2021. Geoscience Knowledge Graph in the Big Data Era. Science China Earth Sciences, 64(7): 1105-1114. https://doi.org/10.1007/s11430-020-9750-4 Zhou, S. Z., Meng, Y., Jin, B. W., et al., 2024. Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction. arXiv, 2402.11142. https://arxiv.org/abs/2402.11142 柏道远, 李彬, 曾广乾, 等, 2025. 江南金矿带湖南段构造控矿特征及动力机制. 大地构造与成矿学, 知网首发. https://link.cnki.net/urlid/44.1595.P.20251222.1628.002 郭飞, 赖鹏, 黄发明, 等, 2024. 基于知识图谱的滑坡易发性评价文献综述及研究进展. 地球科学, 49(5): 1584-1606. doi: 10.3799/dqkx.2023.058 马香菲, 2025. 基于大模型的地质灾害知识抽取方法研究(硕士学位论文). 西宁: 青海师范大学. 邱芹军, 吴亮, 马凯, 等, 2023. 面向灾害应急响应的地质灾害链知识图谱构建方法. 地球科学, 48(5): 1875-1891. 王嘉翔, 2024. 基于大语言模型的斑岩型铜矿找矿标志智能识别研究(硕士学位论文). 长春: 吉林大学. 吴润泽, 李浩, 梅红波, 等, 2025. 基于知识图谱检索增强生成的滑坡监测预警系统. 地球科学, 50(10): 4125-4136. doi: 10.3799/dqkx.2025.127 肖丹, 宋维国, 严志飞, 等, 2025. 湖南雪峰弧形构造带金矿地质特征、成矿规律与成矿模式. 地质与勘探, 61(3): 450-463. 周成虎, 王华, 王成善, 等, 2021. 大数据时代的地学知识图谱研究. 中国科学: 地球科学, 51(7): 1070-1079. -
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