Machine learning prediction of mafic-ultramafic rock-related Cr-spinel formation environments and its application to the tectonic settings of magmatic sulfide deposits

This study uses the random forest machine learning algorithm to classify and predict Cr-spinel formation en-vironments in mafic-ultramafic rocks. Cr-spinel is an early-crystallized oxide phase in these rocks and exhibits significant compositional variations that provide valuable insights into their formation environments. Tradi-tionally, empirical diagrams have been used to classify the origins of Cr-spinel based on major and trace element compositions; however, overlap exists among spinel from different formation environments. To overcome this limitation, we develop a random forest model using a compiled dataset of Cr-spinel compositions from various tectonic settings, including small intrusions from continental collisional zones, layered intrusions and small in-trusions from intra-continental plates (including large igneous provinces and intra-continental rifts), Alaskan-type complexes from oceanic subduction zones, and ophiolites from oceanic spreading centers. The model is trained, validated, and optimized using cross-validation and hyperparameter tuning. The results show high ac-curacy in predicting the Cr-spinel formation environments, as demonstrated by high precision, recall, and F1 scores. Feature importance analyses reveal that elements, such as Ti, Cr, and Al, play a more significant role in the prediction. Visualization techniques, i.e., the t-distributed stochastic neighbor embedding (TSNE) algorithm, are applied to reduce the dimensionality of the dataset and create decision boundaries for better interpretation. The model is validated using an independent Cr-spinel composition dataset obtained from an area with known geological settings, to confirm the model's reliability. The model is applied successfully to reveal the hetero-geneous sources of the rifting-related Daxueshan magmatic Ni-Cu sulfide deposit in the Baoshan block in the eastern part of the Tethyan orogenic belt. The machine learning algorithm can accurately distinguish different Cr-spinel types using large and diverse datasets to provide valuable insights into geological formation conditions. This approach is a powerful tool for understanding Earth's geological processes, improving exploration models of magmatic sulfide deposit and reducing mineral exploration costs.