Comparative machine learning facies prediction using ensemble boosting models and support vector machine versus unsupervised clustering

Abstract This study explores the application of machine learning for facies classification in complex sandstone formations with overlapping petrophysical features. Three boosting ensemble models, Random Forest, XGBoost and CatBoost, were compared with one non-ensemble supervised model, Support Vecto...

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Bibliografische Detailangaben

Hauptverfasser:	Muhammad Risha, Paul Liu
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Springer 2026-01-01
Schriftenreihe:	Discover Geoscience
Schlagworte:	Facies classification Machine learning Geostatistics Synthetic data augmentation Ensemble models
Online-Zugang:	https://doi.org/10.1007/s44288-026-00398-5
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