A STEPWISE INVERSION OF A GROUNDWATER FLOW MODEL WITH MULTI-SCALE OBSERVATION DATA

Based on the regional hydrogeological conditions and the sedimentary architectures in the Los Alamos National Laboratory Site, New Mexico, we established a groundwater flow model with more than 20 stratified hydrofacies. A stepwise inverse method was developed to estimate the model parameters by coupling observation data from different sources and at various spatial scales ranging from single-well test, multiple-well pumping test to regional aquifer long-term monitoring data. To determine the flow parameters for these hydrofacies, we started from statistical analyses of outcrop permeability measurements and single-well slug or pumping test results to define the prior distributions of the parameters. This prior information was used to define the parameter initial values and the lower and upper bounds for inverse modeling. A number of inverse modeling scenarios were conducted including using drawdown data from the PM-2 and PM-4 pump tests separately, and a joint inversion coupling PM-2 and PM-4 pump test data, and head data from regional aquifer long-term monitoring which was started from the mid-1940s. Parameter sensitivity coefficients for different data sets were computed to analyze the parameter identifiability in different scenarios. Finally, the coupled inversion results offer a reasonable fitting to all data sets. The uncertainty of estimated parameters for the hydrofacies is addressed with the eigenvalues of covariance matrix and the parameter confidence intervals. The scale-dependence of permeability is discussed based on the influence ranges of the pumping tests and the spatial scales of the data sets.