Abstract: Cone penetration test(CPT) is usually performed vertically to identify subsurface soil stratification. However,due to time and budget constraints,the number of CPT soundings performed in a site is often limited,leading to a great challenge in properly interpreting CPT data and identifying stratification in unsounded area along horizontal direction. A Bayesian learning method is presented in this paper to address this difficulty. The method can predict soil classification and stratification in a two-dimensional(2D)vertical cross-section using a limited number of CPT soundings. The method consists of three components:(1)2D interpolation of CPT data using Bayesian learning; (2)determination of soil behavior type(SBT)using Robertson chart at every location in the 2D cross-section,including locations with and without CPT soundings; (3) and soil layer/zone delineation using an edge detection method. High-resolution CPT data and SBT information in the 2D vertical cross-section can be obtained. Soil layer/zone boundaries are delineated automatically. The method is illustrated using a simulated example. The results suggest that the method performs well even when only five sets of CPT soundings are available.