矿业科学学报2025，Vol.10Issue(1)：57-69,13.DOI:10.19606/j.cnki.jmst.2024933
基于PSO-XGBoost的煤层断层智能识别方法研究
Research on intelligent fault identification method of coalfield based on the PSO-XGBoost algorithm
摘要
Abstract
In order to further improve the accuracy and efficiency of underground fault identification,an intelligent fault recognition model based on the extreme gradient boosting tree(XGBoost)machine learning algorithm was constructed for coal seam faults,combined with the particle swarm optimization(PSO)algorithm to optimize the model's related parameters.A forward model was established to verify the PSO-XGBoost model,and the classification prediction performance of the PSO-XGBoost model was compared with that of the PSO-RF and PSO-SVM models based on actual data collected from the Dian-dong mining area.The accuracy rate and log loss value were selected as the main evaluation indicators to evaluate the accuracy of the classification prediction models for each model.The results show that the PSO-XGBoost model has a high accuracy in fault structure identification;the PSO-XGBoost model has higher accuracy and better stability in fault identification.关键词
断层识别/XGBoost/PSO/机器学习/参数优化Key words
fault recognition/XGBoost/PSO/machine learning/parameters optimization分类
矿山工程引用本文复制引用
林朋,孙成,任珂,刘育林,李阳..基于PSO-XGBoost的煤层断层智能识别方法研究[J].矿业科学学报,2025,10(1):57-69,13.基金项目
国家重点研发计划(2023YFC3008902) （2023YFC3008902）
