Development and application of a coupled-process parameter inversion modelbased on the maximum likelihood estimation method

The coupled flow-mass transport inverse problem is formulated using the max imum likelihood estimation concept. An evolutionary computational algorithm , the genetic algorithm, is applied to search for a global or near-global s olution. The resulting inverse model allows for flow and transport paramete r estimation, based on inversion of spatial and temporal distributions of h ead and concentration measurements. Numerical experiments using a subset of the three-dimensional tracer tests conducted at the Columbus, Mississippi site are presented to test the model's ability to identify a wide range of parameters and parametrization schemes. The results indicate that the model can be applied to identify zoned parameters of hydraulic conductivity, geo statistical parameters of the hydraulic conductivity field, angle of hydrau lic conductivity anisotropy, solute hydrodynamic dispersivity, and sorption parameters. The identification criterion, or objective function residual, is shown to decrease significantly as the complexity of the hydraulic condu ctivity parametrization is increased. Predictive modeling using the estimat ed parameters indicated that the geostatistical hydraulic conductivity dist ribution scheme produced good agreement between simulated and observed head s and concentrations. The genetic algorithm, while providing apparently rob ust solutions, is found to be considerably less efficient computationally t han a quasi-Newton algorithm. (C) 1999 Elsevier Science Ltd. All rights res erved.