Prior geological knowledge enhanced Markov random field for development of geological cross-sections from sparse data
Qualified subsurface geological cross-sections are indispensable for efficient site planning and risk management of underground infrastructure. Nevertheless, delineating a qualified geological cross-section from sparse site-specific data is challenging, especially when dealing with heterogeneous str...
Saved in:
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180899 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Qualified subsurface geological cross-sections are indispensable for efficient site planning and risk management of underground infrastructure. Nevertheless, delineating a qualified geological cross-section from sparse site-specific data is challenging, especially when dealing with heterogeneous strata patterns and inconsistent stratigraphic information revealed from adjacent boreholes. To address this challenge, this study proposes a knowledge-based geological modelling paradigm to enhance the traditional Markov random field (MRF)-based stratigraphic modelling method. An informed initial stratigraphic configuration is first inferred from a single training image that reflects prior geological knowledge. The configuration is then integrated with the MRF algorithm for stochastic simulations of geological cross-sections. The MRF parameters are estimated from the initial stratigraphic configuration and undergo automatic regularization and updating with constraints from site-specific boreholes in an unsupervised Bayesian manner. The performance of the proposed method is illustrated using real-world examples, including the Zhu-Hai subsea tunnel and Nan-Chang Urban Rail Transit tunnel projects. The results indicate that the model can accurately predict the presence of complex geological patterns, such as interlayer structures and isolated boulder stones, from sparse data with quantified stratigraphic uncertainty. Moreover, the advantages of the proposed model over existing approaches are demonstrated, and the practical significance of prior geological information for stochastic stratigraphic modelling is discussed. |
|---|