Articles | Volume 13, issue 2
Geosci. Model Dev., 13, 651–672, 2020
https://doi.org/10.5194/gmd-13-651-2020
Geosci. Model Dev., 13, 651–672, 2020
https://doi.org/10.5194/gmd-13-651-2020

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 et al.

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AR by Zhen Yin on behalf of the Authors (15 Jan 2020)  Author's response    Manuscript
ED: Publish as is (20 Jan 2020) by Thomas Poulet
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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.