Efficient simulation of 3D conditional random field using kriging with Gaussian-process trend

• Published in: Computers and geotechnics Vol. 177; p. 106862
• Main Authors: Ching, Jianye, Yoshida, Ikumasa
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Gaussian process regression; Probabilistic site characterization; Random field; Spatial variability; Gaussian process regression; Random field; Probabilistic site characterization; Spatial variability
• ISSN: 0266-352X
• DOI: 10.1016/j.compgeo.2024.106862

Previous investigations have shown that for the modeling the soil spatial variability, the Gaussian process regression (GPR) provides a more plausible trend model than the linear combination of basis functions. However, the effectiveness of the conditional random (CRF) simulation based on the GPR trend model (denoted by the t-GPR kriging) has not been investigated. This study first addresses the high computational cost issue of the t-GPR kriging for realisic 3D problems by deriving the Kronecker-product algorithms. Then, this study further investigates the effectiveness of the t-GPR kriging in CRF simulation using real case studies. It is shown that with the Kronecker-product derivations, the computational time can be dramatically reduced such that the t-GPR kriging can conduct CRF simulation for full-scale 3D problems.