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Multivariate Gaussian Process Regression for 3D site characterization from CPT and categorical borehole data
- 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 ofAccurate 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.…


| Autor*innen: | Orestis ZinasORCiD, Iason Papaioannou, Ronald Schneider, Pablo CuéllarORCiD |
|---|---|
| Dokumenttyp: | Zeitschriftenartikel |
| Veröffentlichungsform: | Verlagsliteratur |
| Sprache: | Englisch |
| Titel des übergeordneten Werkes (Englisch): | Engineering Geology |
| Jahr der Erstveröffentlichung: | 2025 |
| Organisationseinheit der BAM: | 7 Bauwerkssicherheit |
| 7 Bauwerkssicherheit / 7.2 Ingenieurbau | |
| Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
| Verlag: | Elsevier B.V. |
| Jahrgang/Band: | 352 |
| Aufsatznummer: | 108052 |
| Erste Seite: | 1 |
| Letzte Seite: | 19 |
| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik |
| Freie Schlagwörter: | Cone Penetration Tests; Geotechnical site-characterization; Linear Model of Coregionalization; Multivariate Gaussian process; Stratigraphy prediction; Variational inference |
| Themenfelder/Aktivitätsfelder der BAM: | Energie |
| Energie / Windenergie | |
| DOI: | 10.1016/j.enggeo.2025.108052 |
| URN: | urn:nbn:de:kobv:b43-629456 |
| ISSN: | 1872-6917 |
| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
| Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
| Datum der Freischaltung: | 14.04.2025 |
| Referierte Publikation: | Ja |
| Datum der Eintragung als referierte Publikation: | 19.05.2025 |
| Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |


