Stochastic stratigraphic simulation using image warping from sparse data

Abstract

Read online

Quantifying stratigraphic uncertainty is crucial for reliable risk assessment and informed decision-making in geotechnical and geological engineering. However, accurately modeling complex stratigraphy—especially in heterogeneous settings influenced by irregular deposition—remains a challenge, particularly with limited site data. This study introduces a novel solution, modeling stratigraphy as a categorical random field and using image warping to transform non-stationary random fields into stationary ones, facilitating fast and realistic stochastic simulation. The method demonstrates high accuracy and computational efficiency in capturing complex stratigraphic profiles with quantified uncertainty. Validation through synthetic and real-world cases confirms the approach’s reliability and applicability.

Keywords

• stratigraphic uncertainty
• non-stationary random field
• image warping
• bayesian machine learning
• markov random field