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    智能矿产预测软件ArcMPM 2.0

    左仁广 师路易

    左仁广, 师路易, 2026. 智能矿产预测软件ArcMPM 2.0. 地球科学, 51(3): 1165-1168. doi: 10.3799/dqkx.2026.089
    引用本文: 左仁广, 师路易, 2026. 智能矿产预测软件ArcMPM 2.0. 地球科学, 51(3): 1165-1168. doi: 10.3799/dqkx.2026.089
    Zuo Renguang, Shi Luyi, 2026. Intelligent Mineral Prospectivity Mapping Software ArcMPM 2.0. Earth Science, 51(3): 1165-1168. doi: 10.3799/dqkx.2026.089
    Citation: Zuo Renguang, Shi Luyi, 2026. Intelligent Mineral Prospectivity Mapping Software ArcMPM 2.0. Earth Science, 51(3): 1165-1168. doi: 10.3799/dqkx.2026.089

    智能矿产预测软件ArcMPM 2.0

    doi: 10.3799/dqkx.2026.089
    基金项目: 

    国家自然科学基金项目 42425208

    国家自然科学基金项目 42530801

    详细信息
      作者简介:

      左仁广(1981—),男,教授,博士生导师,主要从事数学地球科学与智能矿产预测研究. ORCID:0000-0002-5639-3128. E-mail:zrguang@cug.edu.cn

    Intelligent Mineral Prospectivity Mapping Software ArcMPM 2.0

    • 图  1  ArcMPM 2.0界面及功能

      Fig.  1.  The interface and functions of ArcMPM 2.0

      图  2  监督空-谱双分支地球化学空间模式识别与异常提取深度学习模型

      Fig.  2.  The supervised spatial and spectrum dual-branch deep learning model for geochemical spatial pattern recognition and anomaly extraction

      图  3  非监督空-谱双分支地球化学空间模式识别与异常提取深度学习模型

      Fig.  3.  The unsupervised spatial and spectrum dual-branch deep learning model for geochemical spatial pattern recognition and anomaly extraction

      图  4  地质约束自监督图-Transformer矿产预测模型

      Fig.  4.  The geologically constrained self-supervised graph-Transformer model for mineral prospectivity mapping

      图  5  地质约束图强化学习矿产预测模型

      Fig.  5.  The geologically constrained graph reinforcement learning model for mineral prospectivity mapping

    • Elman, J. L., 1990. Finding Structure in Time. Cognitive Science, 14(2): 179-211. https://doi.org/10.1207/s15516709cog1402_1
      Kipf, T. N., Welling, M., 2017. Semi-Supervised Classification with Graph Convolutional Networks. arXiv, 1609.02907. http://arxiv.org/abs/1609.02907
      Li, T., Zuo, R. G., Xiong, Y. H., et al., 2021. Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping. Natural Resources Research, 30(1): 27-38. https://doi.org/10.1007/s11053-020-09742-z
      Meng, Z. Z., Zuo, R. G., 2025. Self-Supervised Graph Contrastive Learning for Mineral Prospectivity Mapping. Mathematical Geosciences, 57(7): 1265-1282. https://doi.org/10.1007/s11004-025-10191-8
      Shi, Z. X., Zuo, R. G., 2026. Improving Geochemical Anomaly Recognition Associated with Mineralization via Geological Knowledge Interacting Reinforcement Learning. Mathematical Geosciences, 58(1): 175-194. https://doi.org/10.1007/s11004-025-10213-5
      Veličković, P., Cucurull, G., Casanova, A., et al., 2018. Graph Attention Networks. arXiv, 1710.10903. http://arxiv.org/abs/1710.10903
      Xu, Y., Shi, L. Y., Zuo, R. G., 2024. Geologically Constrained Unsupervised Dual-Branch Deep Learning Algorithm for Geochemical Anomalies Identification. Applied Geochemistry, 174: 106137. https://doi.org/10.1016/j.apgeochem.2024.106137
      Xu, Y., Zuo, R. G., 2024. An Interpretable Graph Attention Network for Mineral Prospectivity Mapping. Mathematical Geosciences, 56(2): 169-190. https://doi.org/10.1007/s11004-023-10076-8
      Xu, Y., Zuo, R. G., Chen, Z. Y., et al., 2025. Recent Advances and Future Research Directions in Deep Learning as Applied to Geochemical Mapping. Earth-Science Reviews, 270: 105209. https://doi.org/10.1016/j.earscirev.2025.105209
      Yang, F. F., Zuo, R. G., Long, D. H., 2026. A Multiscale Transformer-Graph Attention Network for Geochemical Prospecting. Science China Earth Sciences, Online. https://doi.org/10.1007/s11430-025-1808-1
      Yin, B. J., Zuo, R. G., Xiong, Y. H., 2022. Mineral Prospectivity Mapping via Gated Recurrent Unit Model. Natural Resources Research, 31(4): 2065-2079. https://doi.org/10.1007/s11053-021-09979-2
      Zhang, C. J., Zuo, R. G., Xiong, Y. H., 2021. Detection of the Multivariate Geochemical Anomalies Associated with Mineralization Using a Deep Convolutional Neural Network and a Pixel-Pair Feature Method. Applied Geochemistry, 130: 104994. https://doi.org/10.1016/j.apgeochem.2021.104994
      Zuo, R. G., 2025. Key Technology for Intelligent Mineral Prospectivity Mapping: Challenges and Solutions. Science China Earth Sciences, 68(9): 2976-2991. https://doi.org/10.1007/s11430-025-1622-1
      Zuo, R. G., Shi, L. Y., Yang, F. F., et al., 2024. ArcMPM: An ArcEngine-Based Software for Mineral Prospectivity Mapping via Artificial Intelligence Algorithms. Natural Resources Research, 33(1): 1-21. https://doi.org/10.1007/s11053-023-10286-1
      Zuo, R. G., Xu, Y., 2024. A Physically Constrained Hybrid Deep Learning Model to Mine a Geochemical Data Cube in Support of Mineral Exploration. Computers & Geosciences, 182: 105490. https://doi.org/10.1016/j.cageo.2023.105490
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    • 被引次数: 0
    出版历程
    • 网络出版日期:  2026-04-13
    • 刊出日期:  2026-03-25

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