BRIEF RESEARCH REPORT article
Front. Built Environ.
Sec. Geotechnical Engineering
Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1651919
This article is part of the Research TopicRising Stars in Geotechnical Engineering: Volume 2View all 3 articles
Stochastic Stratigraphic Simulation Using Image Warping from Sparse Data
Provisionally accepted
- 1Hunan Institute of Engineering, Xiangtan, China
- 2University of Dayton, Dayton, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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 realworld cases confirms the approach's reliability and applicability.
Keywords: Stratigraphic uncertainty, Non-stationary random field, Image warping, Bayesian machine learning, markov random field
Received: 22 Jun 2025; Accepted: 25 Jul 2025.
Copyright: © 2025 Wei and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hui Wang, University of Dayton, Dayton, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.