机器学习驱动矿床成因类型判别OA

[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.