矿产预测大模型

师路易; 左仁广

doi:10.3799/dqkx.2025.190

Volume 51 Issue 3

Mar. 2026

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Article Navigation > Earth Science > 2026 > 51(3): 832-848

Shi Luyi, Zuo Renguang, 2026. Foundation Model for Mineral Prospectivity Mapping. Earth Science, 51(3): 832-848. doi: 10.3799/dqkx.2025.190

Citation:

Shi Luyi, Zuo Renguang, 2026. Foundation Model for Mineral Prospectivity Mapping. Earth Science, 51(3): 832-848. doi: 10.3799/dqkx.2025.190

Shi Luyi, Zuo Renguang, 2026. Foundation Model for Mineral Prospectivity Mapping. Earth Science, 51(3): 832-848. doi: 10.3799/dqkx.2025.190

Citation:

Shi Luyi, Zuo Renguang, 2026. Foundation Model for Mineral Prospectivity Mapping. Earth Science, 51(3): 832-848. doi: 10.3799/dqkx.2025.190

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Foundation Model for Mineral Prospectivity Mapping

doi: 10.3799/dqkx.2025.190

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

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

Received Date: 2025-08-18
Publish Date: 2026-03-25

Abstract

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

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References(120)

References

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