Articles | Volume 13, issue 2
Geosci. Model Dev., 13, 651–672, 2020
Geosci. Model Dev., 13, 651–672, 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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Cited articles

Abbott, J.: Technical Report Mineral Resource Estimation for the Wonarah Phosphate Project Northern Territory, Australia, available at: (last access: 13 January 2020), 2013. 
Abdi, H., Williams, L. J., and Valentin, D.: Multiple factor analysis: principal component analysis for multitable and multiblock data sets, WIREs Comp. Stat., 5, 149–179,, 2013. 
Athens, N. D. and Caers, J. K.: A Monte Carlo-based framework for assessing the value of information and development risk in geothermal exploration, Appl. Energ., 256, 113932,, 2019a. 
Aydin, O. and Caers, J. K.: Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework, Tectonophysics, 712, 101–124,, 2017. 
Barfod, A. A. S., Møller, I., Christiansen, A. V., Høyer, A.-S., Hoffimann, J., Straubhaar, J., and Caers, J.: Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods, Hydrol. Earth Syst. Sci., 22, 3351–3373,, 2018. 
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.