Zhou, K.; Sun, T.; Liu, Y.; Feng, M.; Tang, J.; Mao, L.; Pu, W.; Huang, J. Prospectivity Mapping of Tungsten Mineralization in Southern Jiangxi Province Using Few-Shot Learning. Minerals2023, 13, 669.
Zhou, K.; Sun, T.; Liu, Y.; Feng, M.; Tang, J.; Mao, L.; Pu, W.; Huang, J. Prospectivity Mapping of Tungsten Mineralization in Southern Jiangxi Province Using Few-Shot Learning. Minerals 2023, 13, 669.
Zhou, K.; Sun, T.; Liu, Y.; Feng, M.; Tang, J.; Mao, L.; Pu, W.; Huang, J. Prospectivity Mapping of Tungsten Mineralization in Southern Jiangxi Province Using Few-Shot Learning. Minerals2023, 13, 669.
Zhou, K.; Sun, T.; Liu, Y.; Feng, M.; Tang, J.; Mao, L.; Pu, W.; Huang, J. Prospectivity Mapping of Tungsten Mineralization in Southern Jiangxi Province Using Few-Shot Learning. Minerals 2023, 13, 669.
Abstract
Mineral prospectivity mapping (MPM), aiming to outline and prioritize mineral exploration targets, has been spurred by data-driven machine learning algorithms. Supervised data-driven MPM is a typical few-shot task, suffering from the scarcity of labeled data, over-fitting of models and uncertainty of predictions. The main objective of this contribution is to propose a robust framework of few-shot learning (FSL) combining data augmentation and transfer learning, which enables generation of prospectivity models with excellent predictive efficiency and low uncertainty. The mineral systems approach was used to transfer a conceptual mineral system into mappable exploration criteria. Synthetic minority over-sampling technique (SMOTE) was employed to augment and balance the labeled dataset, allowing for model pre-training with a large synthetic training dataset of source domain. The knowledge derived from pre-trained models was then transferred to the target domain by fine-tuning, and the prospectivity model was generated in light of over-fitting and uncertainty assessment. The proposed FSL framework was applied to tungsten prospectivity mapping in southern Jiangxi Province. The results indicate that the SMOTE-ed balanced dataset boosts the classification accuracy in the training process. The FSL models yield an arch-shaped prediction point pattern favorable for focusing potential targets with high probability and low uncertainty. The FSL models achieve a high predictive performance (test AUC=0.9172) and the lowest quantitative over-fitting value, compared to the models derived from the benchmark algorithms of random forest and support vector machine. Four levels of potential targeting zones, considering both predictive efficiency and uncertainty, are extracted from the resulting FSL prospectivity map. The final high-potential and low-risk exploration targets only cover 4.27% of the area, but capture 41.53% known tungsten deposits, achieving superior predictive performance. This study highlights the capability of FSL framework for controlling over-fitting and generating high-confidence exploration targets with low uncertainty.
Keywords
machine learning; mineral prospectivity mapping; few-shot learning; SMOTE; tungsten mineralization; southern Jiangxi Province
Subject
Environmental and Earth Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
