Comparison of Data-Driven Site Characterization Methods in a Real Case History

This study investigated the performance of two data-driven site characterization (DDSC) methods, Glasso and Glasso-BFs, through a benchmark example based on a real case history [labeled as RG1(Adelaide)]. This study focused on root-mean square errors (RMSEs) of qt depth profiles and runtime required for both of model training and validation, and these performance metrics of Glasso and Glasso-BFs were compared. Although two methods can predict qt profiles well, it was difficult to conclude that which method has "higher" performance in the benchmarking. Based on the comparisons, we found that (1) if abrupt changes such as layer boundaries need to be captured, Glasso is recommended; and (2) if 3D subsurface models need to be estimated with reasonable time, it is recommended to use Glasso-BFs.