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      A Sparse Bayesian Estimation Framework for Conditioning Prior Geologic Models to Nonlinear Flow Measurements

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          Abstract

          We present a Bayesian framework for reconstruction of subsurface hydraulic properties from nonlinear dynamic flow data by imposing sparsity on the distribution of the solution coefficients in a compression transform domain.

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          Journal
          0911.4961

          Numerical & Computational mathematics,Geophysics,Mathematical & Computational physics

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