Learning from prior geological information for geotechnical soil stratification with tree-based methods

Geotechnical subsurface stratification based on sparse measurements presents a significant challenge. Learning from prior geological information, such as learning soil layer distribution patterns from stratification results (2D images) of adjacent data-rich projects, is an emerging approach to reducing uncertainties caused by data scarcity. Existing methods rely on pixel-based techniques that require training images to have identical soil types as the testing image. Additionally, pixel-based methods are prone to error accumulation. To address these issues, this study introduces a new framework that focuses solely on learning boundary information from training image rather than soil types. This eliminates the need for matching soil types between training and testing images. Tree-based model is proposed to learn boundary information from training images. Contrary to conventional tree-based models that use coordinates as input, this study employs a set of designed distance fields (boundary dictionary) to represent complex boundary patterns. A selection process is introduced to identify the most important distance fields from the training images. Using these selected distance fields for model input results in soil stratification that aligns well with both borehole data and training image boundaries. The proposed method's efficacy is validated through multiple simulated and real-world cases. The proposed method outperforms pixel-based methods in multiple cases, achieving up to a 25% improvement in accuracy. This method is robust and it yields consistent results for a variety of training images considered. Additionally, the proposed method also provides quantification of interpolation uncertainty through the Gini impurity method.