Abstract

Inverse modeling provides a way whereby measurements of state are used to determine unknown model structures and parameters by fitting model output with measurements.  This technique has been successfully applied to the Los Alamos National Laboratory Site, Los Alamos, New Mexico, 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 monitoring data. A stratified aquifer system with more than 20 hydrofacies was developed based on a detailed hydrogeological framework model. To determine the flow parameters for these hydrofacies, we first conducted statistical analysis 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 andPM-4 pump tests separately, and a joint inversion coupling PM-2 and PM-4 pump test data, and head data from the regional aquifer monitoring. Parameter sensitivity coefficients to different data sets are computed to analyze the parameter identifiability in different scenarios. The scale-dependence of permeability is discussed based on the influence ranges of the pumping tests and the spatial scales of the data sets.  Finally, the joint inversion results offer a reasonable fitting to all these three data sets. The uncertainty of estimated parameters for the hydrofacies is addressed with the eigenvalues of covariance matrix and the parameter confidence intervals.