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Analysis of Tomographic Pumping Tests with Regularized Inversion

Analysis of Tomographic Pumping Tests with Regularized Inversion. Geoffrey C. Bohling Kansas Geological Survey SIAM Geosciences Conference Santa Fe, NM, 22 March 2007. Simultaneous analysis of multiple tests (or stresses) with multiple observation points

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Analysis of Tomographic Pumping Tests with Regularized Inversion

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  1. Analysis of Tomographic Pumping Tests with Regularized Inversion Geoffrey C. Bohling Kansas Geological Survey SIAM Geosciences Conference Santa Fe, NM, 22 March 2007

  2. Simultaneous analysis of multiple tests (or stresses) with multiple observation points Information from multiple flowpaths helps reduce nonuniqueness But still the same inverse problem we have been dealing with for decades Hydraulic Tomography Bohling

  3. Forward and Inverse Modeling • Forward Problem: d = G(m) • Approximate: No model represents true mapping from parameter space (m) to data space (d) • Nonunique: BIG m  small d • Inverse Problem: m = G-1(d) • Effective: Estimated parameters are always “effective” (contingent on approximate model) • Unstable: small d  BIG m Bohling

  4. Regularizing the Inverse Problem • Groundwater flow models potentially have a very large number of parameters • Uncontrolled inversion with many parameters can match almost anything, most likely with wildly varying parameter estimates • Regularization restricts variation of parameters to try to keep them plausible • Regularization by zonation is traditional approach in groundwater modeling Bohling

  5. Tikhonov Regularization (Damped L.S.) Allow a large number of parameters (vector, m), but regularize by penalizing deviations from reference model, mref Balancing residual norm (sum of squared residuals) against model norm (squared deviations from reference model) Increasing regularization parameter, a, gives “smoother” solution Reduces instability of inversion and avoids overfitting data L is identity matrix for zeroth-order regularization, numerical Laplacian for second-order regularization Plot of model norm versus residual norm with varying a is an “L-curve” – used in selecting appropriate level of regularization Bohling

  6. Highly permeable alluvial aquifer (K ~ 1.5x10-3 m/s) Many experiments over past 19 years Induced gradient tracer test (GEMSTRAC1) in 1994 Hydraulic tomography experiments in 2002 Various direct push tests over past 7 years Field Site (GEMS) Bohling

  7. Field Site Stratigraphy From Butler, 2005, in Hydrogeophysics (Rubin and Hubbard, eds.), 23-58 Bohling

  8. Tomographic Pumping Tests Bohling

  9. Drawdowns from Gems4S Tests From Bohling et al., 2007, Water Resources Research, in press Bohling

  10. Analysis Approach • Forward simulation with 2D radial-vertical flow model in Matlab • Vertical “wedge” emanating from pumping well • Common 10 x 14 Cartesian grid of lnK values mapped into radial grid for each pumping well • Inverse analysis with Matlab nonlinear least squares function, lsqnonlin • Fitting parameters are Cartesian grid lnK values • Regularization relative to uniform lnK (K = 1.5 x 10-3 m/s) model for varying values of a • Steady-shape analysis Bohling

  11. Parallel Synthetic Experiments • For guidance, tomographic pumping tests simulated in Modflow using synthetic aquifer • Vertical lnK variogram for synthetic aquifer derived from GEMSTRAC1 lnK profile • Vertical profile includes fining upward trend and periodic (cyclic) component • Large horizontal range (61 m) yields “imperfectly layered” aquifer • K values range from 4.9 x 10-5 m/s (silty to clean sand) to 1.7 x 10-2 m/s (clean sand to gravel) with a geometric mean of 1.2 x 10-3 m/s Bohling

  12. Synthetic Aquifer (81 x 49 x 70) Bohling

  13. Four Grids Bohling

  14. L-curves, Real and Synthetic Tests Bohling

  15. Synthetic Results Bohling

  16. Model Norm Relative to “Truth” Bohling

  17. Real Results Bohling

  18. Transient Fit, Gems4S Using K field for a = 0.025 with Ss = 3x10-5 m-1 Bohling

  19. Transient Fit, Gems4N Using K field for a = 0.025 with Ss = 3x10-5 m-1 Bohling

  20. Well Locations Bohling

  21. Comparison to Other Estimates Bohling

  22. Conclusions • Synthetic results show that steady-shape radial analysis of tests captures salient features of K field, but also indicate “effective” nature of fits • For real tests, pattern of estimated K probably reasonable, although range of estimated values may be too wide • A lot of effort to characterize a 10 m x 10 m section of aquifer; perhaps not feasible for routine aquifer characterization studies • Should be valuable for detailed characterization at research sites Bohling

  23. Acknowledgment s • Field effort led by Jim Butler with support from John Healey, Greg Davis, and Sam Cain • Support from NSF grant 9903103 and KGS Applied Geohydrology Summer Research Assistantship Program Bohling

  24. Regularizing w.r.t. Stochastic Priors Second-order regularization – asking for smooth variations from prior model Fairly strong regularization here (α = 0.1) Best 5 fits of 50 Bohling

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