.png){kind=icon}
返回
Blind submarine seismic deconvolution for long source wavelets
.png){kind=icon}
OA
AI
DOI：10.1109/JOE.2007.899408.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
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)
期刊
IF:
5.3
论文数:
2.6K
被引数:
7.4K
机构
引用论文
Research on the Mechanism of Surfactant Warm Mix Asphalt Additive-Based on Molecular Dynamics Simulation.png)
CoatingsOAAI
CoatingsIF0
OAAI分享收藏Bayesian estimation of state-space models applied to deconvolution of Bernoulli-Gaussian processesSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Crystal chemistry and metal-hydrogen bonding in anisotropic and interstitial hydrides of intermetallics of rare earth (R) and transition metals (T), RT3 and R2T7稀土 (R) 和过渡金属 (T) 的金属间化合物的各向异性和间隙氢化物中的晶体化学和金属氢键，RT3 和R2T7
PREAI
分享收藏
查看更多内容