Probabilistic characterization of subsurface stratigraphic configuration with modified random field approach

Chao Zhao, Wenping Gong, Tianzheng Li, C. Hsein Juang, Huiming Tang, Hui Wang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Accurate and precise characterization of the subsurface stratigraphic configuration (geological model) at a given site is crucial to geotechnical engineering work. The uncertainty in the derived stratigraphic configuration can be significant, due to the strata's complexity and inherent spatial variability coupled with the limited availability of borehole data. The characterization and reduction of this uncertainty should be part of any site characterization project. This paper presents a method for characterization of the subsurface stratigraphic configuration with limited borehole data. Within the framework of the proposed method, the spatial correlation between the existence of a stratum in one subsurface zone and that in the other subsurface zone (or the spatial correlation of the existence of the stratum) is captured by an autocorrelation function determined with the maximum likelihood principle. The initial stratigraphic configurations are first sampled with the conditional random field theory. Next, the maximum-a-posteriori (MAP) estimates of the initial stratigraphic configurations are derived using Markov Chain Monte Carlo (MCMC) and taken as the final stratigraphic realizations. The effectiveness of the proposed method and its advantages over the existing stratigraphic characterization methods are demonstrated through a series of comparative analyses. The versatility of the new approach in modeling the 3-D stratigraphic configuration is further revealed through a case study of a site in Western Australia. This paper adds to the literature on stratigraphic uncertainty characterization and provides a basis for a risk-based geotechnical assessment that considers geological and geotechnical uncertainties.

Original languageEnglish
Article number106138
JournalEngineering Geology
Volume288
DOIs
StatePublished - Jul 2021

Keywords

  • Conditional random field
  • Markov Chain Monte Carlo (MCMC)
  • Monte Carlo simulation (MCS)
  • Spatial correlation
  • Stratigraphic uncertainty

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