Development of training image database for subsurface stratigraphy

ABSTRACT

Image-based stochastic simulation methods, such as multiple point statistics (MPS), can be viewed as a physics-informed Bayesian learning approach, which samples typical stratigraphic patterns from a single training image for onward conditional modelling of subsurface stratigraphy. A training image is essentially a prior geological model, which comprises representative stratigraphic connectivity at the site of interest. One key difficulty hindering wide application of image-based geological modelling methods is the lack of qualified training images. In this study, a systematic framework is proposed to develop training image databases for conditional simulations of subsurface stratigraphy. Collected training images can be further categorised based on three key factors, namely, geological origin, site location and application scenario. As a pilot study, a total of 54 geological cross-sections, mainly interpreted by experienced engineering practitioners, for weathered granite and tuff slopes in Hong Kong are collected and compiled as two training image databases. To demonstrate value and application of the established training image databases, subsurface stratigraphy for real weathered granite slope examples are used as illustrative examples, and stratigraphic uncertainty is also quantified. Results indicate that training image databases are of great significance for subsurface stratigraphy and uncertainty quantification, particularly when only limited site-specific data are available.

KEYWORDS:

• Uncertainty quantification
• database-driven stratigraphy
• multiple point statistics
• database compilation

Acknowledgements

The authors would like to thank Mr. Jiachun Zhou, Wuhao Huang and Ms. Ya Wen for helping digitalise geological cross-sections.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The database can be downloaded from https://sites.google.com/site/yuwangcityu.

Additional information

Funding

The work described in this paper was supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region (Project no. CityU 11202121), a grant from the Innovation and Technology Commission of Hong Kong Special Administrative Region (Project No: MHP/099/21), and a grant from Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology (Category C) Project No: SGDX20210823104002020), China. The financial support is gratefullly acknowledged.