1. Bayesian and Geostatistical Approaches to Inverse Problems Peter K. Kitanidis Civil and Environmental Engineering Stanford University

2. 2 Outline: Important points Current Work

3. 3 Inverse Problem: Estimate functions from sparse and noisy observations; The unknowns are sensitive to data gaps or flaws (Problem is ill-posed in the sense of Hadamard); Data are insufficient to zero in on a unique solution; Usually, it is the small-scale variability that cannot be resolved.

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5. 5 Cheney, M. (1997), Inverse boundary-value problems, American Scientist, 85: 448-455.

6. 6 Bayesian Inference Applied to Inverse Modeling Posterior distribution of unknown parameter Prior distribution of unknown parameter Likelihood of unknown parameter given data y : measurements s : “unknown”

7. 7 Bayesian Inference Applied to Inverse Modeling Combined information (data and structure) Information about structure Information from observations y : measurements s : “unknown”

8. 8 How do you get the structure? We often use an “empirical Bayes” in which the structure pdf is parameterized and inferred from the data; the approach is rigorous and robust. –Alternative interpretation: We use cross- validation. In specific applications, we may use “geological” or other information to describe structure.

9. 9 Computational cost Reduce cost by dealing with special cases, or Bite the bullet and use computer intensive numerical methods (MCMC, etc.)

10. 10 A source identification problem Identify the pumping rate at an extraction well from head observations, in a neighboring monitoring well: The importance of properly weighing observations

11. 11 Over-weighting Observations Five slides from: Kitanidis, P. K. (2007), On stochastic inverse modeling, in Subsurface Hydrology Data Integration for Properties and Processes edited by D. W. Hyndman, F. D. Day-Lewis and K. Singha, pp. 19-30, AGU, Washington, D. C.

12. 12 Under-weighting Observations

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15. “Optimal” Weighting

16. 16 The cost of computations… Moore’s law: Cost of computations is halved every 1.5 years. Thus, between 1975 and 2006: 2^(31/1.5)=1.7E6. $5,000 of computer usage for a project in 1975. 1975$5,000 -- adjust for inflation -> 2006$20,000. $20,000/1.7E6 corresponds to 1 cent worth of computational power in 2006.

17. 17 From the BOISE HYDROGEOPHYSICAL RESEARCH SITE (BHRS)

18. 18 Based on Michalak and Kitanidis (2003 and 2004) Use EM method on marginal distributions to find optimal parameters for structure and epistemic error. Employ a Gibbs sampler to build a set of conditional realizations of posterior pdf. (A large enough set of conditional realizations has the same statistical properties as the actual posterior distribution.) METHOD—Markov Chain Monte Carlo

19. 19 PCE data at location PPC13. Measurement data and fitted concentrations resulting from the estimated boundary conditions. Michalak, A.M., and P.K. Kitanidis (2003) “A Method for Enforcing Parameter Nonnegativity in Bayesian Inverse Problems with an Application to Contaminant Source Identification,” Water Resources Research, 39(2), 1033, doi:10.1029/2002WR001480. A problem of forensic environmental engineering

20. 20 Location PPC13. Estimated time variation of boundary concentration at the interface between the aquifer and aquitard. The end time represents the sampling date (June 6, 1996).

21. 21 PCE data at location PPC13 with non-negativity constraint. Measurement data and fitted concentrations resulting from the estimated boundary conditions.

22. 22 Location PPC13 with non-negativity constraint. Estimated time variation of boundary concentration at the interface between the aquifer and aquitard. The end time represents the sampling date (June 6, 1996).

23. 23 TRACER RESPONSE—Synthetic Case Output Without Error With Error Tracer Input True Transfer Function Fienen, M. N., J. Luo, and P. Kitanidis (2006), A Bayesian Geostatistical Transfer Function Approach to Tracer Test Analysis, Water Resour. Res., 42, W07426, 10.1029/2005WR004576.

24. 24 Current Work Large variance and highly nonlinear problems (Convergence of Gauss- Newton, usefulness of Fisher matrix, etc.) Tomographic inverse problems (development of protocols, processing of large data sets.)

25. 25 Current Work (cont.) Identification of zone boundaries. Solution methods for very large data sets. Making tools available to users.

26. 26 Identification of zone boundaries: Example Linear tomography Zones + small-scale variability measurement error (2%) Four slides from the work of Michael Cardiff

27. 27 Example Problem Performance

28. 28 We are developing… Stochastic analysis of zone uncertainty Merging of structural (level set) and geostatistical inverse problem concepts Use of level sets for joint inversion

29. 29 Toolbox for COMSOL Multiphysics is a commercial general purpose PDE solver. We are adding inverse-model capabilities, including adjoint-state sensitivity analysis and stochastics. See: Cardiff, M, and P. K. Kitanidis, “Efficient solution of nonlinear underdetermined inverse problems with a generalized PDE solver”, Computers and Geosciences, in review, 2007.

30. 30 Stochastic: (aka probabilistic or statistical): We assign a probability to every possible solution. Our approach is: Bayesian: Because the Bayesian approach provides a general framework. Practical: Our methods are evolving, with particular emphasis on practicality, robustness, and computational efficiency. Geostatistical: We have adopted the best ideas from the geostatistical school.

31. 31 For More Info See publications on the WWW: http://www.stanford.edu/group/peterk/publications.htm