- 中文版：
- EI 100%收录
- 英文版
- SCI 100%收录
中国出版政府奖提名奖
中国百强科技报刊
湖北出版政府奖
中国高校百佳科技期刊
中国最美期刊
中国出版政府奖提名奖
中国百强科技报刊
湖北出版政府奖
中国高校百佳科技期刊
中国最美期刊
| 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
|
|
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.
|
|
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.
|
|
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
|
Figures(5)
DownLoad:
