Aquifer structure identification using stochastic inversion

This study presents a stochastic inverse method for aquifer structure identification using sparse geophysical and hydraulic response data. The method is based on updating structure parameters from a transition probability model to iteratively modify the aquifer structure and parameter zonation. The method is extended to the adaptive parameterization of facies hydraulic parameters by including these parameters as optimization variables. The stochastic nature of the statistical structure parameters leads to nonconvex objective functions. A multi-method genetically adaptive evolutionary approach (AMALGAM-SO) was selected to perform the inversion given its search capabilities. Results are obtained as a probabilistic assessment of facies distribution based on indicator cokriging simulation of the optimized structural parameters. The method is illustrated by estimating the structure and facies hydraulic parameters of a synthetic example with a transient hydraulic response.

[1]  Y. Rubin,et al.  Spatial correlation of permeability in cross‐stratified sediment with hierarchical architecture , 2004 .

[2]  J. Samper,et al.  Inverse modeling of water flow and multicomponent reactive transport in coastal aquifer systems , 2006 .

[3]  Ming Ye,et al.  Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff , 2003 .

[4]  Richard L. Cooley,et al.  Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 2. Applications , 1983 .

[5]  G. Fogg,et al.  Modeling Spatial Variability with One and Multidimensional Continuous-Lag Markov Chains , 1997 .

[6]  Zhenxue Dai,et al.  Representing aquifer architecture in macrodispersivity models with an analytical solution of the transition probability matrix , 2007 .

[7]  N. Sun.  Structure reduction and robust experimental design for distributed parameter identification , 2005 .

[8]  S. P. Neuman,et al.  Estimation of aquifer parameters under transient and steady-state conditions: 2 , 1986 .

[9]  Bruce A. Robinson,et al.  Parameter identification using the level set method , 2006 .

[10]  Y. Rubin,et al.  An effective Bayesian model for lithofacies estimation using geophysical data , 2003 .

[11]  Zhenxue Dai,et al.  Improving permeability semivariograms with transition probability models of hierarchical sedimentary architecture derived from outcrop analog studies , 2005 .

[12]  B. Robinson,et al.  Summary of the Models and Methods for the FEHM Application-A Finite-Element Heat- and Mass-Transfer Code , 1997 .

[13]  Richard L. Cooley,et al.  Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 1. Theory , 1982 .