Blind submarine seismic deconvolution for long source wavelets

被引：10

作者：

Nsiri, Benayad ^{[1
,2
,3
]}

Chonavel, Thierry ^{[4
]}

Boucher, Jean-Marc ^{[4
,5
]}

Nouze, Herve ^{[6
]}

机构：

[1] Univ Hassan 2, Dept Phys, Ain Chock Fac Sci, Casablanca 20101, Morocco

[2] Lab Rech Informat & Teelcommunicat, Grp Signaux Commun & Multimedia, Rabat, Morocco

[3] Ecole Natl Super Telecommun Bretagne, F-29200 Brest, France

[4] Ecole Natl Super Telecommun Bretagne, Dept Signal & Commun, F-29200 Brest, France

[5] Natl Sci Res Ctr Lab, Traitement Algorithm & Mat Commun Informat & Conn, UMR 2872, F-29238 Brest, France

[6] IFREMER, Ctr Brest, Marine Geosci Dept, F-29280 Plouzane, France

来源：

IEEE JOURNAL OF OCEANIC ENGINEERING | 2007年 / 32卷 / 03期

关键词：

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);

D O I：

10.1109/JOE.2007.899408

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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.

引用

页码：729 / 743

页数：15

共 38 条

[1]

[Anonymous], 1987, DIGITAL FILTER DESIG

[2]

[Anonymous], 1985, Computational Statistics Quarterly, DOI DOI 10.1155/2010/874592

[3]

BESAG J, 1986, J R STAT SOC B, V48, P259

[4]

BOUJIDA M, 1996, P 8 EUR SIGN PROC C, V1, P137

[5] Blind identification using the kurtosis with applications to field data [J].

Boumahdi, M .

SIGNAL PROCESSING, 1996, 48 (03) :205-216

[6] Stochastic versions of the EM algorithm: An experimental study in the mixture case [J].

Celeux, G ;

Chauveau, D ;

Diebolt, J .

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 1996, 55 (04) :287-314

[7]

CELEUX G, 1989, STOCHASTICS STOCHAST, V22, P747

[8] AN ITERATIVE GIBBSIAN TECHNIQUE FOR RECONSTRUCTION OF M-ARY IMAGES [J].

CHALMOND, B .

PATTERN RECOGNITION, 1989, 22 (06) :747-761

[9] Simultaneous wavelet estimation and deconvolution of reflection seismic signals [J].

Cheng, QS ;

Chen, R ;

Li, TH .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1996, 34 (02) :377-384

[10]

CHONAVEL T, 2002, STAT SIGNAL PROCESSI, P166

← 1 2 3 4 →