ABSTRACT
Data-driven site characterization (DDSC) is defined as any site characterization methodology that relies solely on measured data, both site-specific data collected for the current project and existing data of any type collected from past stages of the same project or past projects at the same site, neighboring sites, or beyond. One key complication is that real data is “ugly”. A useful mnemonic is MUSIC-3X (Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted with “3X” denoting three dimensional spatial variations). It is an open question whether DDSC can solve real world subsurface mapping problems based on real world MU-SIC-3X data with minimum ad-hoc assumptions. The Sparse Bayesian Learning (SBL) approach is very promising, particularly since it is nearly data-driven and it can handle a large scale 3D problem without incurring excessive cost. This 3D SBL would be made available in Rocscience’s Settle3 (three-dimensional soil settlement analysis) in the near future. On the research front, the hunt is on for a “holy grail” mapping approach that is fully data-driven, MUSIC-3X compliant, and is able to exploit all available data including data from similar sites.
