Benchmarking 3D Subsurface Models from Bayesian Compressive Sampling Using Real Data

Three-dimensional (3D) subsurface models for precise characterization of complex site conditions have attracted increasing attention recently. Various methods for 3D subsurface modelling have been developed. However, these methods are often validated using synthetic data, rather than real data, for example, cone penetration test (CPT) data. In addition, how to evaluate and compare the performance of these 3D methods in a fair and quantitative manner remains an open question. This may impede the application of 3D modelling methods in engineering practices. To address these issues, this paper presents a benchmarking study to evaluate the performance of 3D modelling methods on real site conditions using real CPT data from different sites. An in-house software called Analytics of Sparse Spatial Data by Bayesian compressive sampling/sensing (ASSD-BCS) is used in the benchmarking study for illustration. ASSD-BCS can directly generate 3D random field samples (RFSs) from sparse measurements. The performance of ASSD-BCS is evaluated in terms of accuracy, uncertainty, robustness, and computational efficiency. The evaluation results show that ASSD-BCS provides accurate prediction results with quantified uncertainty. More importantly, ASSD-BCS has remarkably high computational efficiency and performs robustly.