Machine Learning Driven Discrimination of Ore Genesis Types: A Case Study of the Hua'aobaote Pb-Zn-Ag Deposit in the southern Great Xing’an Range

Objective Accurately identifying the genesis of mineral deposits is a crucial step in mineral exploration. Traditional methods, relying on expert experience and limited data, are often constrained by inefficiencies and accuracy limitations. The trace elements in sphalerite can precisely reflect the ore-forming environment, serving as key indicators for distinguishing the genesis of Pb-Zn deposits. To improve the efficiency and accuracy of genesis classification, exploring the application of low-code machine learning methods in geological data analysis is essential. Methods This study utilizes the PyCaret machine learning library in combination with trace element data from sphalerite to build a classification model for deposit genesis, covering porphyry, skarn, and hydrothermal vein types. The model is applied to the Hua'aobaote Pb-Zn-Ag deposit in the Great Xing’an Range. Results After comparing multiple models, the ET and LightGBM models performed the best, achieving accuracies of 97.59% and 96.88%, respectively. Feature importance analysis revealed that Co and Fe elements play a crucial role in distinguishing deposit genesis and are significant factors influencing model decisions. Conclusions Based on the model results and previous studies, the Hua'aobaote deposit is classified as a low-temperature, shallow hydrothermal vein deposit associated with a porphyry-style magmatic-hydrothermal system. This study demonstrates the feasibility and practicality of low-code machine learning methods for ore deposit genesis analysis, offering an effective technological approach for rapid and accurate genesis classification.