Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 2
Articles | Volume 13, issue 2
https://doi.org/10.5194/gmd-13-651-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-651-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 2
Development and technical paper
 | 
19 Feb 2020
Development and technical paper |  | 19 Feb 2020

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

Zhen Yin, Sebastien Strebelle, and Jef Caers

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Short summary
We provide completely automated Bayesian evidential learning (AutoBEL) for geological uncertainty quantification. AutoBEL focuses on model falsification, global sensitivity analysis, and statistical learning for joint model uncertainty reduction by borehole data. Application shows fast and robust uncertainty reduction in geological models and predictions for large field cases, showing its applicability in subsurface applications, e.g., groundwater, oil, gas, and geothermal or mineral resources.
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