A Bregman adaptive sparse-spike deconvolution method in the frequency domain

To improve the anti-noise performance of the time-domain Bregman iterative algorithm, an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed. By solving the Bregman algorithm in the frequency domain, the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided. In other words, the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain. Moreover, the computational efficiency is greatly improved compared with the conventional method. Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation. Different theoretical models are built and solved using the algorithms in both time and frequency domains. Finally, the proposed and the conventional methods are both used to process actual seismic data. The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.

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