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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Cited articles

Baddeley, M. C., Curtis, A., and Wood, R.: An introduction to prior information derived from probabilistic judgements: elicitation of knowledge, cognitive bias and herding, Geological Society, London, Special Publications, 239, 15–27, 2004. a
Bardossy, G. and Fodor, J.: Evaluation of Uncertainties and Risks in Geology: New Mathematical Approaches for Their Handling, Springer Science & Business Media, 2013. a
Bistacchi, A., Massironi, M., Dal Piaz, G. V., Dal Piaz, G., Monopoli, B., Schiavo, A., and Toffolon, G.: 3D Fold and Fault Reconstruction with an Uncertainty Model: An Example from an Alpine Tunnel Case Study, Comput. Geosc., 34, 351–372,, 2008. a
Bolstad, W. M.: Understanding Computational Bayesian Statistics, John Wiley & Sons, 2009. a, b
Bond, C., Gibbs, A., Shipton, Z., and Jones, S.: What Do You Think This Is? “Conceptual Uncertainty” in Geoscience Interpretation, GSA Today, 17, 4,, 2007. a, b
Short summary
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.