A coarse-to-fine approach for intelligent logging lithology identification with extremely randomized trees

DOI10.1007/S11004-020-09885-YzbMath1472.86044OpenAlexW3047770725MaRDI QIDQ2238076

Runshan Hu, Yunxin Xie, Chenyang Zhu, Zhengwei Zhu

Publication date: 29 October 2021

Published in: Mathematical Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11004-020-09885-y

zbMATH Keywords

ensemble methodsoutlier detectionlithology classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Geological problems (86A60)

Uses Software

• randomForest
• AdaBoost-SAMME
• LOF
• XGBoost
• MobileNets

Cites Work

• Multi-class AdaBoost
• Data Mining and Knowledge Discovery Handbook
• Extremely randomized trees
• Stochastic gradient boosting.

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