Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 6
Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 6
Development and technical paper
 | 
28 Jun 2021
Development and technical paper |  | 28 Jun 2021

Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1

Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond

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Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
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