Lithology identification method based on the PCA dimension reduction and improved PSO-XGBoost algorithm

Traditional lithology identification methods can not meet the actual needs due to the low identification accuracy and the poor generalization capability which were resulted from the imbalanced lithology distribution under the complicated geological conditions. With the rapid development of artificia...

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Bibliographic Details

Published in:	Bulletin of Mineralogy Petrology and Geochemistry
Main Authors:	Han-han YANG, Jing-zhe LI, Xiu-fan ZHANG, Z. H. Wang
Format:	Article
Language:	English
Published:	2026
Subjects:	Geochemistry and Geologic Mapping Hydrocarbon exploration and reservoir analysis Machine Learning in Materials Science lithology identification class imbalance XGBoost PCA dimensionality reduction mudstone sandstone
Online Access:	https://www.sciengine.com/doi/pdf/829D889CDCC84CEB93AA0F669E2D841E https://doi.org/10.3724/j.issn.1007-2802.20250147
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