We describe a method that allows for blind surface consistent estimation of the source and receiver wavelets of seismic signals. This is very relevant for surface-consistent deconvolution where current processing standards focus on the removal of the source and receiver effects under the minimum phase assumption. The proposed method, which is an extension of the Euclid deconvolution method, employs an iterative algorithm that simultaneously estimates the source and receiver wavelets that are consistent with the data. Unlike most deconvolution methods, the algorithm requires no prior phase assumptions. Another important feature of the algorithm is that we questioned the Gaussian density assumption of the reflectivity series and instead implemented a sparse regularizer to constrain the solution space of our desired reflectivity series. In other words, we assume that the reflectivity series can be cast as a sparse vector with few nonzero coefficients.
['Nasser Kazemi', 'Emmanuel Bongajum', 'Mauricio D. Sacchi']
Surface-Consistent Sparse Multichannel Blind Deconvolution of Seismic Signals