Publications

SNL-NUMO collaborative : development of a deterministic site characterization tool using multi-model ranking and inference

Arnold, Bill W.; Gray, Genetha A.; Grace, Matthew G.; Ahlmann, Michael A.

Uncertainty in site characterization arises from a lack of data and knowledge about a site and includes uncertainty in the boundary conditions, uncertainty in the characteristics, location, and behavior of major features within an investigation area (e.g., major faults as barriers or conduits), uncertainty in the geologic structure, as well as differences in numerical implementation (e.g., 2-D versus 3-D, finite difference versus finite element, grid resolution, deterministic versus stochastic, etc.). Since the true condition at a site can never be known, selection of the best conceptual model is very difficult. In addition, limiting the understanding to a single conceptualization too early in the process, or before data can support that conceptualization, may lead to confidence in a characterization that is unwarranted as well as to data collection efforts and field investigations that are misdirected and/or redundant. Using a series of numerical modeling experiments, this project examined the application and use of information criteria within the site characterization process. The numerical experiments are based on models of varying complexity that were developed to represent one of two synthetically developed groundwater sites; (1) a fully hypothetical site that represented a complex, multi-layer, multi-faulted site, and (2) a site that was based on the Horonobe site in northern Japan. Each of the synthetic sites were modeled in detail to provide increasingly informative 'field' data over successive iterations to the representing numerical models. The representing numerical models were calibrated to the synthetic site data and then ranked and compared using several different information criteria approaches. Results show, that for the early phases of site characterization, low-parameterized models ranked highest while more complex models generally ranked lowest. In addition, predictive capabilities were also better with the low-parameterized models. For the latter iterations, when more data were available, the information criteria rankings tended to converge on the higher parameterized models. Analysis of the numerical experiments suggest that information criteria rankings can be extremely useful for site characterization, but only when the rankings are placed in context and when the contribution of each bias term is understood.