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<p>Data-driven MPM (mineral prospectivity mapping) of critical minerals, which are elements or minerals with strategic importance and high supply chain risk, is vital for national land use planning. Recently, MPM has been practiced using supervised machine learning classification algorithms. However, applying such algorithms to Canadian critical minerals presents two major challenges. The first stems from the nature of geological knowledge, which is primarily stored in unstructured text. However, most supervised machine learning algorithms struggle to directly incorporate this textual information into predictive models. The second challenge arises from the limited number of known mineral deposits associated with many critical minerals in Canada, resulting in insufficient training labeled data for supervised classification tasks. To address the first challenge, this study employed natural language processing (NLP) techniques and large language models (LLMs) to extract and transform geoscientific knowledge embedded in geoscience text corpora into predictive features for MPM. LLMs operate based on transformer deep learning architectures that use &#xa0;self-attention mechanisms to capture contextual relationships within natural&#xa0;language. A domain-specific LLM, which was fine-tuned in this study and evaluated using geology-related inquiries, was employed for MPM. To address the second challenge, a separate transformer model was developed using a self-supervised learning approach that integrates diverse geophysical, geochronological, and textual data, &#xa0;eliminating the dependency on a substantial number of&#xa0;labeled training samples. The prospectivity model generated using the proposed transformer model significantly reduced the search space—by an average of 87%—for the targeted type of mineral deposits. The findings of this study demonstrate the effectiveness of transformer-based architectures and LLMs in overcoming key limitations of modern MPM approaches for critical mineral exploration.</p>

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Large Language Models and Geoscience Transformers for Predictive Mapping of Canadian Critical Minerals

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Mohammad Parsa,
  • Renato Cumani,
  • Hossein Jodeiri Akbari Fam,
  • Bilal Tawbe

摘要

Data-driven MPM (mineral prospectivity mapping) of critical minerals, which are elements or minerals with strategic importance and high supply chain risk, is vital for national land use planning. Recently, MPM has been practiced using supervised machine learning classification algorithms. However, applying such algorithms to Canadian critical minerals presents two major challenges. The first stems from the nature of geological knowledge, which is primarily stored in unstructured text. However, most supervised machine learning algorithms struggle to directly incorporate this textual information into predictive models. The second challenge arises from the limited number of known mineral deposits associated with many critical minerals in Canada, resulting in insufficient training labeled data for supervised classification tasks. To address the first challenge, this study employed natural language processing (NLP) techniques and large language models (LLMs) to extract and transform geoscientific knowledge embedded in geoscience text corpora into predictive features for MPM. LLMs operate based on transformer deep learning architectures that use  self-attention mechanisms to capture contextual relationships within natural language. A domain-specific LLM, which was fine-tuned in this study and evaluated using geology-related inquiries, was employed for MPM. To address the second challenge, a separate transformer model was developed using a self-supervised learning approach that integrates diverse geophysical, geochronological, and textual data,  eliminating the dependency on a substantial number of labeled training samples. The prospectivity model generated using the proposed transformer model significantly reduced the search space—by an average of 87%—for the targeted type of mineral deposits. The findings of this study demonstrate the effectiveness of transformer-based architectures and LLMs in overcoming key limitations of modern MPM approaches for critical mineral exploration.