Transform-domain sparsity regularization for inverse problems in geosciences

• Main Authors: Jarapour, Behnam (Author), Goyal, Vivek K. (Contributor), McLaughlin, Dennis (Contributor), Freeman, William T. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Society of Exploration Geophysicists, 2012-01-03T19:40:43Z.
• Subjects: Article
• Online Access: Get fulltext

 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 regulariza-tion approach, it has performed better than traditional alter-natives.