相关实验视频

Updated: May 12, 2025

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography
12:18

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

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HELIOS-Stack:一种新的混合集体学习方法,用于在岩石不连续性分析中精确预测关节粗度系数

Ibrahim Haruna Umar1,2, Hang Lin1, Hongwei Liu1

  • 1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.

Materials (Basel, Switzerland)
|May 7, 2025
PubMed
概括

一种新的混合组合学习方法 (HELIOS-Stack) 准确地预测了岩石质量的关节粗度系数 (JRC). 这种先进的技术改善了地质表面分析和岩石力学理解.

关键词:
高斯混合模型 (GMM)这就是HELIOS-Stack.关节粗度系数 关节粗度系数机器学习是机器学习.预测建模预测建模岩石的不连续性 岩石的不连续性

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