TY - JOUR A1 - Zinas, Orestis A1 - Papaioannou, Iason A1 - Schneider, Ronald A1 - Cuéllar, Pablo T1 - Multivariate Gaussian Process Regression for 3D site characterization from CPT and categorical borehole data N2 - Accurate prediction of subsurface stratigraphy and geotechnical properties, along with quantification of associated uncertainties, is essential for improving the design and assessment of geotechnical structures. Several studies have utilized indirect data from Cone Penetration Tests (CPTs) and employed statistical and Machine Learning methods to quantify the geological and geotechnical uncertainty. Incorporating direct borehole data can reduce uncertainties. This study proposes a computationally efficient multivariate Gaussian Process model that utilizes site-specific data and: (i) jointly models multiple categorical (USCS labels) and continuous CPT variables, (ii) learns a non-separable covariance structure leveraging the Linear Model of Coregionalization, and (iii) predicts a USCS based stratigraphy and CPT parameters at any location within the 3D domain. The results demonstrate that integrating geotechnical and geological data into a unified model yields more reliable predictions of subsurface stratification, enabling the parallel interpretation of both USCS classification and CPT profiles. Importantly, the model demonstrates its potential to integrate multiple variables from different sources and data types, contributing to the advancement of methodologies for the joint modeling of geotechnical, geological, and geophysical data. KW - Geotechnical site-characterization KW - Cone Penetration Tests KW - Stratigraphy prediction KW - Multivariate Gaussian process KW - Variational inference KW - Linear Model of Coregionalization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629456 DO - https://doi.org/10.1016/j.enggeo.2025.108052 SN - 1872-6917 VL - 352 SP - 1 EP - 19 PB - Elsevier B.V. AN - OPUS4-62945 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -