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A fast algorithm for sparse multichannel blind deconvolution
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DOI：10.1190/GEO2015-0069.1.png)
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We have addressed blind deconvolution in a multichannel framework. Recently, a robust solution to this problem based on a Bayesian approach called sparse multichannel blind deconvolution (SMBD) was proposed in the literature with interesting results. However, its computational complexity can be high. We have proposed a fast algorithm based on the minimum entropy deconvolution, which is considerably less expensive. We designed the deconvolution filter to minimize a normalized version of the hybrid l(1)/l(2)-norm loss function. This is in contrast to the SMBD, in which the hybrid l(1)/l(2)-norm function is used as a regularization term to directly determine the deconvolved signal. Results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework. Similar results were also obtained in a real marine data set for both techniques.
Keyword:
MINIMUM ENTROPY DECONVOLUTION
WAVELET ESTIMATION
SIGNALS
SYSTEMS
ART
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