A Machine Learning Zircon Trace Element Tool to Predict Porphyry Deposit Type and Resource Size - Wen, Zi‐Hao; Xu, Bo; Kirkland, Christopher L.; Lentz, David R.; Hou, Zeng‐Qian; Wang, Tao; Yuan, Mao‐Wen (Journal of geophysical research. Machine learning and computation, 2024)
Porphyry deposits are primarily known for their association with base metals like copper and to some extent molybdenum and gold. Here we present machine learning models, based on zircon composition, that provide quantitative distinction between different deposit types and resource sizes. Using a global zircon compositional database for different porphyry deposits (9,649 samples), we trained several machine learning models. A porphyry deposit type model (PDT model) was developed using XGBoost, which distinguishes between barren, Cu, and Mo bearing deposits. Furthermore, porphyry Cu and Mo reserve models (Porphyry Cu Reserve [PCR] and Porphyry Mo Reserve [PMR] model) were also developed using XGBoost and LightGBM, respectively, to give prediction of resource size in unexplored area. F1‐scores for the models are 0.97, 0.91, and 0.82. The model‐built feature importance and Shapley Additive exPlanations values imply that (EuN/EuN*)/Y, Th/U, Th/U and Ce are important in the PDT model, Ti, T (°C), U, and Hf are important for the PCR model, and Hf, U, Th/U, and EuN/EuN* are important for the PMR model. From a mineral system perspective, the three models imply that water, temperature, and magma evolution are pivotal to the type of deposits that forms. Temperature and magma evolution in particular are important in prediction of Cu and Mo resource size. Application of models to the Wunugetushan deposit gives ore type and resource predictions that are consistent with known deposit occurrence and geochemistry. These findings suggest that machine learning models may not only assist in understanding the main geological processes linked to porphyry mineralization, but also have application in reducing exploration risk. Plain Language Summary Zircon is considered an indicator mineral for recording the formation and mineralization process of porphyry deposits because its trace elements are sensitive to physicochemical changes in fertile magmas. However, the prediction of mineralization types and metal resources using zircon trace elements remains difficult for conventional two‐dimensional investigation. Here we apply machine learning approaches to develop a porphyry deposit type model (PDT) and Cu and Mo resource size models (Porphyry Cu Reserve [PCR] and Porphyry Mo Reserve [PMR]). The models are based on a global zircon database for porphyry deposits which have known metal reserve information. F1‐scores, combining precision and recall, evaluates the performance of the classification models. F1‐scores for the PDT, PCR, and PMR models are 0.97, 0.91, and 0.82, respectively. To understand the primary controls on these models, we deployed built‐in feature importance and Shapley Additive exPlanations value analyses. These tools imply that water, temperature, and magma evolution are important in distinguishing deposit types, with the latter two factors more important for resource size prediction. In a case study of the Wunugetushan porphyry deposit, zircon classification models yield probabilities of ore deposit types and metal resources, showing potential application to mineral exploration. Key Points Porphyry deposit type, Cu reserve, and Mo reserve models are developed using XGBoost and LightGBM Feature significance analysis reveals that water, temperature, and magmatic evolution are important in predicting mineralization A case study from the Wunugetushan deposit demonstrates the potential of zircon based classification models in mineral systems