Probabilistic identification of soil stratigraphy using CPT data

Abstract

The deterministic approach for interpreting CPT soil profiles poses the serious limitation of not taking data uncertainty into account. Therefore, a Bayesian model was developed by Wang et al. (2013) that, for a given CPT profile, determines the most probable number of soil layers and most probable soil layer thicknesses by simulating and comparing multiple ‘model classes’ with different complexities. In this study, this proposed model is implemented into the Python coding environment after which the functionality is verified by conducting a case study on a 23 푚 CPT profile from the Groningen area (NE Netherlands). For the given CPT profile, the model distinguishes 6 separate soil layers from which the position and thickness are in agreement with the deterministic analysis and the available borehole data. However, the case study suggests that the model fails to correctly identify the most probable soil types for CPT measurements within the vicinity of the edges of the Robertson chart. This is most-likely related to a “cut-off”-effect of the joint Gaussian distribution describing the uncertainty of a single datapoint. A subsequent study on the integration of the statistical parameters within the model is therefore required. Additionally, the code includes several optimizing strategies, but remains time consuming for very complex model classes. Further optimization is suggested to achieve greater model precision and efficiency.