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

✍ Scribed by Alex S. Mayer; Changlin Huang

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 468 KB
• Volume: 22
• Category: Article
• ISSN: 0309-1708
• DOI: 10.1016/s0309-1708(98)00049-9

✦ Synopsis

The coupled ¯ow-mass transport inverse problem is formulated using the maximum likelihood estimation concept. An evolutionary computational algorithm, the genetic algorithm, is applied to search for a global or near-global solution. The resulting inverse model allows for ¯ow and transport parameter estimation, based on inversion of spatial and temporal distributions of head 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, geostatistical parameters of the hydraulic conductivity ®eld, angle of hydraulic conductivity anisotropy, solute hydrodynamic dispersivity, and sorption parameters. The identi®cation criterion, or objective function residual, is shown to decrease signi®cantly as the complexity of the hydraulic conductivity parametrization is increased. Predictive modeling using the estimated parameters indicated that the geostatistical hydraulic conductivity distribution scheme produced good agreement between simulated and observed heads and concentrations. The genetic algorithm, while providing apparently robust solutions, is found to be considerably less ecient computationally than a quasi-Newton algorithm.