Simultaneous wavelet estimation and deconvolution of reflection seismic signals

Qiansheng Cheng, Rong Chen, Ta Hsin Li

Research output: Contribution to journalArticle

62 Citations (Scopus)

Abstract

In this paper, the problem of simultaneous wavelet estimation and deconvolution is investigated with a Bayesian approach under the assumption that the reflectivity obeys a Bernoulli-Gaussian distribution. Unknown quantities, including the seismic wavelet, the reflection sequence, and the statistical parameters of reflection sequence and noise are all treated as realizations of random variables endowed with suitable prior distributions. Instead of deterministic procedures that can be quite computationally burdensome, a simple Monte Carlo method, called Gibbs sampler, is employed to produce random samples iteratively from the joint posterior distribution of the unknowns. Modifications are made in the Gibbs sampler to overcome the ambiguity problems inherent in seismic deconvolution. Simple averages of the random samples are used to approximate the minimum mean-squared error (MMSE) estimates of the unknowns. Numerical examples are given to demonstrate the performance of the method.

Original languageEnglish (US)
Pages (from-to)377-384
Number of pages8
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume34
Issue number2
DOIs
StatePublished - Dec 1 1996

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Deconvolution
deconvolution
wavelet
seismic reflection
sampler
Gaussian distribution
Random variables
reflectivity
Monte Carlo methods
distribution
method

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

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abstract = "In this paper, the problem of simultaneous wavelet estimation and deconvolution is investigated with a Bayesian approach under the assumption that the reflectivity obeys a Bernoulli-Gaussian distribution. Unknown quantities, including the seismic wavelet, the reflection sequence, and the statistical parameters of reflection sequence and noise are all treated as realizations of random variables endowed with suitable prior distributions. Instead of deterministic procedures that can be quite computationally burdensome, a simple Monte Carlo method, called Gibbs sampler, is employed to produce random samples iteratively from the joint posterior distribution of the unknowns. Modifications are made in the Gibbs sampler to overcome the ambiguity problems inherent in seismic deconvolution. Simple averages of the random samples are used to approximate the minimum mean-squared error (MMSE) estimates of the unknowns. Numerical examples are given to demonstrate the performance of the method.",
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Simultaneous wavelet estimation and deconvolution of reflection seismic signals. / Cheng, Qiansheng; Chen, Rong; Li, Ta Hsin.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 2, 01.12.1996, p. 377-384.

Research output: Contribution to journalArticle

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