Reflection coefficient recovery from non-stationary seismic signal by sparse constraint has attracted a lot of attentions. Nevertheless, geologists and geophysicists prefer to acoustic impedance (AI) for reservoir prediction. AI is not sparse itself, so we cannot adopt the reflection coefficient recovery algorithm, under the premise of sparseness of the parameters to be inverted, to retrieve AI directly from non-stationary seismic signals. Fortunately, AI is sparse in total variation (TV) domain under the assumption that the underground medium is layered. In this case, we should perform transform domain sparse constraint to retrieve AI. In this paper, we propose an algorithm for viscoelastic AI recovery by isotropy total variation (ITV) domain sparse regularization in frequency domain. The inversion objective function, with ITV domain sparse constraint, is efficiently optimized by split Bregman algorithm. Finally, our proposed method is testified using synthetic and field data set. The resolution of the AI model retrieved by our method is improved and is helpful for thin beds seismic prediction.


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