Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits - Rui-Chang Tan; Yong-Jun Shao; Yi-Qu Xiong; Zhi-Wei Fan; Hong-Fei Di; Zhao-Jun Wang; Kang-Qi Xu (Applied Sciences)
The chemical composition of apatite has been utilized as an indicator of magmatic fertility related to tungsten mineralization in skarn systems. In this study, we compiled 5776 apatite trace element data from 374 intrusions, along with records indicating magmatic fertility. Then we trained and validated machine learning (ML) models, specifically support vector machine (SVM) and random forests (RF), to classify magmatic fertility based on apatite chemistry in igneous rocks. RF model achieved high classification accuracies (~93%) on the test dataset, demonstrating that employing ML approaches to distinguish apatite derived from fertile versus barren magmas is feasible and effective. Furthermore, we optimized classification thresholds to maximize the model’s predictive accuracy for identifying potentially fertile magmas. Feature-importance analysis of the machine learning classifier shows that elevated La, Yb, and Mn, together with depleted Sr, Y, Gd, and Tb, constitute the most diagnostic elemental signatures of magmatic fertility. As a case study, we applied our trained ML model to predict the magmatic fertility of apatite samples from the Nanling Range (southern China’s largest skarn-type tungsten mineralization province). Benefiting from the application of GAN-based techniques to address sample imbalance, our ML models can effectively identify tungsten-mineralized favorable skarn areas. Additionally, the visualization technique t-distributed stochastic neighbor embedding (t-SNE) was employed to validate and assess classification outcomes. Results showed clear separation between fertile and barren categories within the reduced 3D space. Our findings emphasize apatite as a sensitive indicator mineral for granite-related magmatic fertility and metallogenesis, underscoring its significant potential in mineral exploration. Finally, we provide a convenient prediction software for magmatic fertility based on a machine learning model utilizing apatite trace element compositions.