Improving Sparse Multichannel Blind Deconvolution with Correlated Seismic Data: Foundations and Further Results

For more than half a century, seismic deconvolution has been of a great interest in reflection seismics. Its goal is to remove the effect of the seismic wavelet, i.e., the waveform produced by the seismic source, from the data. Its importance to the industry is that it increases the resolution of the seismic image, yielding a more interpretable seismic section. From a theoretical standpoint, what is interesting is that neither the wavelet nor the characteristics of the subsurface are known, which makes the problem particularly challenging. Over the years, several seismic deconvolution approaches have been proposed, based on different assumptions about the seismic wavelet and the reflectivity series.

For more than half a century, seismic deconvolution has been of a great interest in reflection seismics. Its goal is to remove the effect of the seismic wavelet, i.e. the waveform produced by the seismic source, from the data. Its importance to the industry is that it increases the resolution of the seismic image, yielding a more interpretable seismic section. From a theoretical standpoint, what is interesting is that neither the wavelet nor the characteristics of the subsurface are known, which makes the problem particularly challenging. Over the years, several seismic deconvolution approaches have been proposed, based on different assumptions about the seismic wavelet and the reflectivity series.