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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Feb 2020
Development and technical paper |
| 19 Feb 2020
| 19 Feb 2020
Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)
Department of Geological Sciences, Stanford University, Stanford, CA
94305, USA
Sebastien Strebelle
Chevron, Houston, USA
Jef Caers
Department of Geological Sciences, Stanford University, Stanford, CA
94305, USA
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Latest update: 29 Jun 2024
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.
We provide completely automated Bayesian evidential learning (AutoBEL) for geological...
