Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff

• Published in: Water resources research Vol. 40; no. 5; pp. W05113 - n/a
• Main Authors: Ye, Ming, Neuman, Shlomo P., Meyer, Philip D.
• Format: Journal Article
• Language: English
• Published: American Geophysical Union 01-05-2004; Blackwell Publishing Ltd
• Subjects: conceptual model uncertainty; cross validation; drift; Groundwater hydrology; Hydrology; Permeability and porosity; Physical Properties of Rocks; predictive performance; predictive uncertainty; stochastic continuum; Stochastic processes; Unsaturated zone; USA, Arizona; stochastic continuum; conceptual model uncertainty; predictive uncertainty; cross validation; predictive performance; drift
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2003WR002557

Hydrologic analyses typically rely on a single conceptual‐mathematical model. Yet hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Adopting only one of these may lead to statistical bias and underestimation of uncertainty. Bayesian model averaging (BMA) [Hoeting et al., 1999] provides an optimal way to combine the predictions of several competing models and to assess their joint predictive uncertainty. However, it tends to be computationally demanding and relies heavily on prior information about model parameters. Neuman [2002, 2003] proposed a maximum likelihood version (MLBMA) of BMA to render it computationally feasible and to allow dealing with cases where reliable prior information is lacking. We apply MLBMA to seven alternative variogram models of log air permeability data from single‐hole pneumatic injection tests in six boreholes at the Apache Leap Research Site (ALRS) in central Arizona. Unbiased ML estimates of variogram and drift parameters are obtained using adjoint state maximum likelihood cross validation [Samper and Neuman, 1989a] in conjunction with universal kriging and generalized least squares. Standard information criteria provide an ambiguous ranking of the models, which does not justify selecting one of them and discarding all others as is commonly done in practice. Instead, we eliminate some of the models based on their negligibly small posterior probabilities and use the rest to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. We then average these four projections and associated kriging variances, using the posterior probability of each model as weight. Finally, we cross validate the results by eliminating from consideration all data from one borehole at a time, repeating the above process and comparing the predictive capability of MLBMA with that of each individual model. We find that MLBMA is superior to any individual geostatistical model of log permeability among those we consider at the ALRS.