Revision as of 15:16, 6 June 2024

Abstract

In engineering geology and geotechnical engineering, subsurface soils and rocks are natural geomaterials and exhibit inherent variability in stratigraphy due to geological deposition process. Explicit knowledge of subsurface stratigraphy is a critical input for the analysis, design, and construction of geotechnical engineering systems. However, the accurate and reliable modelling of subsurface geological stratigraphy is challenging due to the limited number of available boreholes in practice and the complex nature of soil stratigraphy. This paper presents an innovative machine learning framework built upon the neighborhood aggregation technique for the prediction of digitized subsurface geological stratigraphy. To predict the stratigraphy at a given point of interest, neighborhood aggregation is first performed to intelligently consolidate the stratigraphy information from its neighboring boreholes, resulting in additional features associated with the target location. By combining the extra stratigraphy information with conventional location-specific features, the framework enhances the predictive capabilities of classical machine learning models at a finer scale. The proposed framework is implemented using common machine learning models and is validated using a simulated benchmark 3D example. The results of leave-one-out cross-validation demonstrate that the proposed framework can improve the performance of classical machine learning models, leading to more reasonable stratigraphy transition and associated uncertainty quantification.