A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning
The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI AG
2020-07-01
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| coleção: | Energies |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/1996-1073/13/15/3903 |
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