矿产预测大模型

师路易; 左仁广

doi:10.3799/dqkx.2025.190

矿产预测大模型

doi: 10.3799/dqkx.2025.190

师路易^,,
左仁广^, ,

中国地质大学(武汉)地质过程与成矿预测全国重点实验室, 湖北武汉 430078

基金项目:

国家自然科学基金项目 42530801

国家自然科学基金项目 42425208

详细信息

作者简介:
师路易（2002-），男，硕士研究生，主要从事数学地质与矿产勘查方面的研究. ORCID：0009-0002-0974-6530. E-mail：1821408640@qq.com

通讯作者:
左仁广，ORCID: 0000-0002-5639-3128. E-mail: zrguang@cug.edu.cn

中图分类号: P577
计量
- 文章访问数: 756
- HTML全文浏览量: 86
- PDF下载量: 137
- 被引次数: 0
出版历程
- 收稿日期: 2025-08-18
- 刊出日期: 2026-03-25

Foundation Model for Mineral Prospectivity Mapping

Shi Luyi^,,
Zuo Renguang^{,
,}

State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (Wuhan), Wuhan 430078, China

摘要

摘要:
大数据人工智能驱动的矿产预测已成为矿产勘查的重要方向，但现有方法普遍存在泛化能力弱、迁移性差、可解释性不足等问题，难以实现跨区域应用.以“预训练-微调”为核心范式的大模型在自然语言处理、计算机视觉等领域展现出卓越的跨任务迁移能力和强泛化性，为突破上述瓶颈提供了可行路径.研发矿产预测大模型，对创新找矿范式、提升勘查效率具有重要探索价值，是智能矿产预测发展的新方向.本文聚焦大语言模型、视觉大模型及多模态大模型的技术特性，系统梳理了大模型的发展历程与构建流程.在此基础上，剖析了现有矿产预测大模型的技术路径与局限，探讨了面向矿产预测语言、视觉和多模态大模型的构建思路，分析了构建矿产预测大模型面临的挑战，为进一步研发矿产预测大模型提供参考.
- 矿产预测 /
- 大数据 /
- 人工智能 /
- 大模型
Abstract:
Mineral prospectivity mapping (MPM) driven by big data and artificial intelligence (AI) represents a cutting-edge approach in mineral exploration. However, exisiting methods are typically constrained by the inability to achieve stable cross-regional applications due to the limited generalization ability, poor transferability, and insufficient interpretability. Foundation models, based on the "pretrain and fine-tune" paradigm, have demonstrated excellent cross-task transfer and strong generalization ability in the fields such as natural language processing and computer vision, offering a promising path to overcome the aforementioned bottlenecks. The development of foundation models for MPM has significant potential to revolutionize traditional models and improve exploration efficiency, representing a new research direction for intelligent MPM. This study systematically reviews the development and construction processes of foundation models, focusing on the state-of-the-art technical characteristics of large language models, visual foundation models, and multimodal foundation models. This study also summarizes the limitations of existing foundation models for MPM, and explores the construction process of MPM foundation models from a perspective of language-based, visual-based and multimodal-based foundation models, and discusses the challenges of developing MPM foundation models, providing a reference for development of MPM foundation models.
- mineral prospectivity mapping /
- big data /
- artificial intelligence /
- foundation model

HTML全文

图 1 Transformer架构示意图(修改自Vaswani et al., 2017)

Fig. 1. The architecture of the Transformer architecture (modified by Vaswani et al., 2017)

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图 2 大语言模型架构示意图

Fig. 2. The architecture of the large language model

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图 3 大语言模型预训练任务示意图

Fig. 3. The architecture of pre-training tasks for large language models

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图 4 基于ViT的视觉大模型示意图(修改自Dosovitskiy et al., 2020)

Fig. 4. The architecture of the visual foundation model based on ViT (modified by Dosovitskiy et al., 2020)

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图 5 视觉大模型预训练任务示意图

Fig. 5. The architecture of pre-training tasks for visual foundation models

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图 6 基于CLIP的多模态大模型预训练任务示意图(修改自Radford et al., 2021)

Fig. 6. The architecture of pre-training tasks for multimodal foundation models based on CLIP (modified by Radford et al., 2021)

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图 7 多模态大语言模型架构

Fig. 7. The architecture of the multimodal large language model

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图 8 MineAgent示意图(修改自Yu et al., 2024)

Fig. 8. The architecture of MineAgent (modified by Yu et al., 2024)

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图 9 GFM4MPM示意图(修改自Daruna et al., 2024)

Fig. 9. The architecture of GFM4MPM (modified by Daruna et al., 2024)

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图 10 FM4GAI示意图(修改自Shi and Zuo, 2026)

Fig. 10. The architecture of FM4GAI (modified by Shi and Zuo, 2026)

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图 11 矿产预测语言大模型示意图

Fig. 11. The architecture of the large language model for mineral prospectivity mapping

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图 12 矿产预测视觉大模型示意图

Fig. 12. The architecture of the visual foundation model for mineral prospectivity mapping

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图 13 找矿文本‒空间预训练数据集

Fig. 13. Mineral exploration text-spatial pre-training dataset

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图 14 找矿文本‒空间微调数据集

Fig. 14. Mineral exploration text-spatial fine-tuning dataset

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图 15 矿产预测多模态大模型预训练示意图

Fig. 15. The architecture of pre-training for the mineral prospectivity mapping multimodal foundation model

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表 1 MineAgent、GFM4MPM和FM4GAI技术路线对比

Table 1. Comparison of technical roadmaps: MineAgent, GFM4MPM and FM4GAI

路线	代表	训练数据	主要优势	主要局限	适用性
多模态大模型+ Agent	MineAgent	多波段遥感数据	训练成本低，实现简单，流程自动化	未用专业找矿数据系统微调；对隐伏构造识别有限；概率输出缺少地质约束	适合快速部署与流程自动化
从零构建矿产预测视觉大模型	GFM4MPM	北美/澳大利亚地质与地球物理数据，铅锌矿监督标签	空间模式学习强，目标任务表现好	文本信息One-Hot编码致语义丢失；输入特征固化，跨区适配性受限	适合同构数据区域的高精度预测
从零构建地球化学异常识别大模型	FM4GAI	39种地球化学特征	学习单个地球化学特征图的空间模式	缺少成矿知识约束，结果可解释性差，不确定性高	可用于具有不同地球化学特征的研究区

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