Data/hora: 16/06/2024 10:43:31

Provedor de dados: 	5	
País: 	France	
Título: 	Blind submarine seismic deconvolution for long source wavelets	
Autores: 	Nsiri, Benayad; Chonavel, Thierry; Boucher, Jean; Nouze, Herve.	
Data: 	2007-07	
Ano: 	2007	
Palavras-chave: 	Bernoulli Gaussian BG process; Blind deconvolution; Gibbs sampler; Maximum likelihood ML; Maximum posterior mode MPM; Monte Carlo Markov chains MCMCs methods; Prony algorithm; Seismic deconvolution; Stochastic expectation maximization SEM.	
Resumo: 	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.	
Tipo: 	Text	
Idioma: 	Inglês	
Identificador: 	http://archimer.ifremer.fr/doc/00000/11030/8978.pdf

DOI:10.1109/JOE.2007.899408	
Editor: 	Ieee-inst Electrical Electronics Engineers Inc	
Relação: 	http://archimer.ifremer.fr/doc/00000/11030/	
Formato: 	application/pdf	
Fonte: 	Ieee Journal Of Oceanic Engineering (0364-9059) (Ieee-inst Electrical Electronics Engineers Inc), 2007-07 , Vol. 32 , N. 3 , P. 729-743	
Fonte: 	2007 IEEE