DocumentCode :
1050842
Title :
Blind Submarine Seismic Deconvolution for Long Source Wavelets
Author :
Nsiri, Benayad ; Chonavel, Thierry ; Boucher, Jean-Marc ; Nouzé, Hervé
Author_Institution :
Univ. Hassan II, Brest
Volume :
32
Issue :
3
fYear :
2007
fDate :
7/1/2007 12:00:00 AM
Firstpage :
729
Lastpage :
743
Abstract :
In seismic deconvolution, blind approaches must be considered in situations where reflectivity sequence, source wavelet signal, and noise power level are unknown. In the presence of long source wavelets, strong interference among the reflectors contributions makes the wavelet estimation and deconvolution more complicated. In this paper, we solve this problem in a two-step approach. First, we estimate a moving average (MA) truncated version of the wavelet by means of a stochastic expectation-maximization (SEM) algorithm. Then, we use Prony´s method to improve the wavelet estimation accuracy by fitting an autoregressive moving average (ARMA) model with the initial truncated wavelet. Moreover, a solution to the wavelet initialization problem in the SEM algorithm is also proposed. Simulation and real-data experiment results show the significant improvement brought by this approach.
Keywords :
Gaussian processes; Markov processes; Monte Carlo methods; autoregressive moving average processes; blind source separation; deconvolution; expectation-maximisation algorithm; geophysical signal processing; oceanographic techniques; seismology; wavelet transforms; Bernoulli-Gaussian process; Gibbs sampler; Monte Carlo Markov chains methods; Prony method; autoregressive moving average model; blind submarine seismic deconvolution; long source wavelets; maximum likelihood estimation; maximum posterior mode; moving average truncated wavelet; noise power level; reflectivity sequence; reflector interference; source wavelet signal; stochastic expectation-maximization algorithm; wavelet estimation; wavelet initialization problem; Acoustic waves; Associate members; Autoregressive processes; Deconvolution; Geology; Reflectivity; Stochastic processes; Surface acoustic waves; Underwater vehicles; Yield estimation; Bernoulli–Gaussian (BG) process; Gibbs sampler; Monte Carlo Markov chains (MCMCs) methods; Prony algorithm; blind deconvolution; maximum likelihood (ML); maximum posterior mode (MPM); seismic deconvolution; stochastic expectation–maximization (SEM);
fLanguage :
English
Journal_Title :
Oceanic Engineering, IEEE Journal of
Publisher :
ieee
ISSN :
0364-9059
Type :
jour
DOI :
10.1109/JOE.2007.899408
Filename :
4443169
Link To Document :
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