Bagging-based positive-unlabeled learning algorithm with Bayesian hyperparameter optimization for three-dimensional mineral potential mapping

Mineralization is a rare event. Hence, the geosciences datasets used for three-dimensional (3D) mineral potential mapping (MPM) are often imbalanced, consisting of scarce positive samples and abundant unlabeled data. Compared with selecting positive samples, it is challenging to select reliable negative samples in 3D MPM. However, the application of supervised machine learning algorithms in 3D MPM requires balanced positive and negative samples. Consequently, semi-supervised machine learning algorithms, which are only trained on positive samples, are widely used in 3D mineral prospecting. In this study, the bagging-based positive-unlabeled learning (PUL) algorithm that utilizes positive samples and unlabeled data in the training process was developed and applied to produce a 3D gold (Au) potential map in the Wulong Au district, China. This study employed Bayesian hyperparameter optimization to tune the hyperparameters of the bagging-based PUL algorithm. The performance of the bagging-based PUL algorithm was further compared with that of the widely used random forest, weights-of-evidence, one-class support vector machine, and continuous weighting approach in 3D MPM. The results demonstrated that the bagging-based PUL algorithm outperformed the aforementioned widely used predictive methods. The 3D mineral targets obtained by the bagging-based PUL algorithm can be beneficial for subsurface Au exploration in the Wulong Au district of China.

Automated summary

Mineralization is rare, leading to imbalanced geoscience datasets for 3D mineral potential mapping, with challenges in selecting reliable negative samples. Therefore, semi-supervised machine learning algorithms are widely used in this field.