Stratification identification and prediction of missing CPT data by Mixture of Gaussian Processes

Abstract

Stratification identification and spatial interpolation play a fundamental role in geotechnical site characterization. A unified approach is needed to perform these two tasks simultaneously to reduce overall uncertainty in site characterization. This paper explores the applicability of the Mixture of Gaussian Processes (MoGP) to address this gap, with a specific focus on characterizing and completing missing CPT data. The investigation encompasses both synthetic and real-world field CPT datasets and includes a comparison of the MoGP's interpolation accuracy with the use of a single GP for entire datasets. Additionally, the study examines the sensitivity of the model's performance with respect to the number of training data points. Although the interpolation performance of the MoGP model is promising with synthetic data, limitations appear in its application to real-site CPT data.