We have developed a new regularization approach for estimating unknown spatial fields, such as facies distributions or porosity maps. The proposed approach is especially efficient for fields that have a sparse representation when transformed into a complementary function space (e.g., a Fourier space). Sparse transform representations provide an accurate characterization of the original field with a relatively small number of transformed variables. We use a discrete cosine transform (DCT) to obtain sparse representations of fields with distinct geologic features, such as channels or geologic formations in vertical cross section. Low-frequency DCT basis elements provide an effectively reduced subspace in which the sparse solution is searched. The low-dimensional subspace is not fixed, but rather adapts to the data. The DCT coefficients are estimated from spatial observations with a variant of compressed sensing. The estimation procedure minimizes an l2-norm measurement misfit term while maintaining DCT coefficient sparsity with an l1-norm regularization term. When measurements are noise-dominated, the performance of this procedure might be improved by implementing it in two steps — one that identifies the sparse subset of important transform coefficients and one that adjusts the coefficients to give a best fit to measurements. We have proved the effectiveness of this approach for facies reconstruction from both scattered- point measurements and areal observations, for crosswell traveltime tomography, and for porosity estimation in a typical multiunit oil field. Where we have tested our sparsity regularization approach, it has performed better than traditional alter-natives.

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