Maximum likelihood Bayesian averaging of airflow models in unsaturated fractured tuff using Occam and variance windows

摘要: We use log permeability and porosity data obtained from single-hole pneumatic packer tests in six boreholes drilled into unsaturated fractured tuff near Superior, Arizona, to postulate, calibrate compare five alternative variogram models (exponential, exponential with linear drift, power, truncated power based on modes, Gaussian modes) of these parameters four model selection criteria (AIC, AICc, BIC KIC). Relying primarily KIC cross-validation we select the first three them parameterize air across site via kriging terms their values at selected pilot points some measurement locations. For each estimate permeabilities porosities by calibrating a finite volume pressure simulator against two cross-hole sets sixteen site. The traditional Occam’s window approach conjunction AIC, assigns posterior probability nearly 1 model. A recently proposed variance does same when applied but spreads more evenly among used KIC. abilities individual MLBMA, both Occam windows, predict space–time variations observed during other than those employed for calibration. Individual largest probabilities turned out be worst or second predictors validation cases. Some predicted pressures accurately did MLBMA. MLBMA was far superior any one test last predictive coverage scores.