Interpolation and stratification of multilayer soil property profile from sparse measurements using machine learning methods

Abstract

Identification of subsurface stratification and characterization of spatially varying soil properties profiles in multiple soil layers are indispensable in geotechnical site investigation. Subsurface soils are often stratified first through visual inspection of soil samples obtained from boreholes or soil behavior type index from cone penetration tests. Then, the soil property profile within each layer is characterized via interpolation of the data points measured within the corresponding layer. Although this stratification first procedure is commonly used in engineering practice, it is difficult to apply when measured data points within each layer are sparse and limited (e.g., only a few data points within each layer), a scenario often encountered in geotechnical site characterization. When the number of measurements within each layer is small, it is difficult to properly interpolate spatially varying soil property profiles in each layer. To address this difficulty and increase the number of data points for enabling proper interpolation, an interpolation first procedure is proposed which utilizes measurements from all layers as input for interpolation, followed by soil stratification using the interpolation results. The proposed procedure includes two key elements: (1) a Bayesian supervised learning method for interpolation of non-stationary data from multiple layers, and (2) an unsupervised machine learning method (e.g., clustering) for soil stratification. An index is also proposed to determine when the proposed method is beneficial and performs better than the stratification first procedure in engineering geology practice. Both numerical and real-life data are used to illustrate the proposed method.