A platform for research: civil engineering, architecture and urbanism
Surface-Consistent Sparse Multichannel Blind Deconvolution of Seismic Signals
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.
Surface-Consistent Sparse Multichannel Blind Deconvolution of Seismic Signals
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.
Surface-Consistent Sparse Multichannel Blind Deconvolution of Seismic Signals
Kazemi, Nasser (author) / Bongajum, Emmanuel / Sacchi, Mauricio D
2016
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Surface-Consistent Sparse Multichannel Blind Deconvolution of Seismic Signals
Online Contents | 2016
|Multichannel Seismic Deconvolution
Online Contents | 1993
|Blind Deconvolution of Impacting Signals
British Library Conference Proceedings | 1999
|Multichannel Seismic Deconvolution Using Markov-Bernoulli Random-Field Modeling
Online Contents | 2009
|Blind modal identification of structures from spatially sparse seismic response signals
Tema Archive | 2014
|