Abstract
Apex shift hyperbolic Radon transform (ASHRT) is an extension of hyperbolic Radon transform (HRT). We have developed a novel sparsity-promoting framework for ASHRT by employing curvelet transform (CT) in the sparse inversion. RT-based seismic data processing can be considered as an optimization problem and a mixed norms inversion, therefore, objective function with CT can promote the sparsity of the transformed domain, which makes the sparse inversion more efficient. Compared with the conventional sparse inversion of ASHRT, the proposed method weights the sparse penalization, which indicates a sparser solution of ASHRT. We use synthetic and field data examples to demonstrate the performance of ASHRT. Compared to the conventional solution, the ours may lead to more accurately reconstructed results and have a better noise immunity.
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