Two-stage Blind Deconvolution
نویسندگان
چکیده
In seismic data processing, deconvolution plays a very important role because it permits to increase the temporal resolution of seismic sections and to equalize sources. The deconvolution problem when the wavelet is known is an ill-posed problem that can be tackled via regularization methods. However, the seismic source wavelet is unknown and therefore, it must be estimated from the data prior to deconvolution. In this paper, we examine an algorithm to simultaneously estimate the reflectivity and the wavelet. The method assumes that the underlying seismic reflectivity is a sparse series and that a common seismic wavelet exists for a large number of seismograms with different reflectivity sequences. The method reduces to the alternating minimization of a cost function to promote sparsity in the reflectivity and smoothness in the wavelet.
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