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<p>The scarcity of labeled samples and pronounced spatial heterogeneity present significant challenges for mineral prospectivity mapping using geoscientific data. This study introduces GeoKIM, a label-informed masked representation learning framework designed for tabular geoscientific datasets under few-shot conditions. During pretraining, the model learns from unlabeled observations through masked feature reconstruction, while limited labeled information is used to construct a correlation-guided masking distribution and to supervise an auxiliary regression branch targeting key metallogenic indicators such as Au, As, and Sb. Evaluation on real-world geochemical data from the Xiahe–Hezuo region is conducted using a spatially aware few-shot protocol with strict spatial partitioning to prevent information leakage. In addition to classical machine learning baselines, GeoKIM is compared with four representative modern tabular learning frameworks, namely FT-Transformer, TabNet, SubTab, and VIME. The results show that, although no method is universally dominant in the extreme 1-shot regime, GeoKIM achieves the best overall accuracy, area under the receiver operating characteristic (ROC) curve (AUC), and F1-score from 5-shot onward and yields the clearest AUC advantage under limited supervision. Ablation results indicate that correlation-guided masking and the Transformer backbone both contribute to downstream performance, while representation analyses suggest improved latent-space organization and reconstruction of mineralization-related indicators. These findings position GeoKIM as a promising label-informed pretraining framework for few-shot mineral prospectivity mapping on tabular geoscientific data.</p>

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GeoKIM: Label-Informed Masked Representation Learning for Geoscientific Data Under Few-Shot Conditions

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Yixiao Wu,
  • Bingli Liu,
  • Cheng Li,
  • Yunhui Kong

摘要

The scarcity of labeled samples and pronounced spatial heterogeneity present significant challenges for mineral prospectivity mapping using geoscientific data. This study introduces GeoKIM, a label-informed masked representation learning framework designed for tabular geoscientific datasets under few-shot conditions. During pretraining, the model learns from unlabeled observations through masked feature reconstruction, while limited labeled information is used to construct a correlation-guided masking distribution and to supervise an auxiliary regression branch targeting key metallogenic indicators such as Au, As, and Sb. Evaluation on real-world geochemical data from the Xiahe–Hezuo region is conducted using a spatially aware few-shot protocol with strict spatial partitioning to prevent information leakage. In addition to classical machine learning baselines, GeoKIM is compared with four representative modern tabular learning frameworks, namely FT-Transformer, TabNet, SubTab, and VIME. The results show that, although no method is universally dominant in the extreme 1-shot regime, GeoKIM achieves the best overall accuracy, area under the receiver operating characteristic (ROC) curve (AUC), and F1-score from 5-shot onward and yields the clearest AUC advantage under limited supervision. Ablation results indicate that correlation-guided masking and the Transformer backbone both contribute to downstream performance, while representation analyses suggest improved latent-space organization and reconstruction of mineralization-related indicators. These findings position GeoKIM as a promising label-informed pretraining framework for few-shot mineral prospectivity mapping on tabular geoscientific data.