Multichannel seismic deconvolution

Idier, Jérôme; Goussard, Yves

doi:10.1109/36.263767

Cited by 45 publications

(29 citation statements)

References 20 publications

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“…However, Idier and Goussard [15] showed that the a posteriori likelihood p T / , T − , T \ , Q, R |Y can be expressed as:…”

Section: A Deconvolution Schemementioning

confidence: 99%

“…The Markov-Bernoulli-Gaussian I reflectivity model [15] is a 2D extension of the 1D Bernoulli-Gaussian representation. It is composed of a Markov-Bernoulli random field, which controls the geometrical characteristics of the reflectivity, and an amplitude field, defined conditionally to the MBRF.…”

Section: B Prior Modelmentioning

confidence: 99%

“…Idier and Goussard [15] proposed two versions of a multichannel deconvolution method which takes into account the stratification of the layers. The two versions are based on two 2D reflectivity models: Markov-Bernoulli-Gaussian (MBG) I and II.…”

Section: Introductionmentioning

confidence: 99%

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Multichannel Deconvolution of Seismic Signals Using Statistical MCMC Methods

Ram

Cohen

Raz

2010

IEEE Trans. Signal Process.

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Abstract-In this paper we propose two multichannel blind deconvolution algorithms for the restoration of two-dimensional (2D) seismic data. Both algorithms are based on a 2D reflectivity prior model, and use iterative multichannel deconvolution procedures which deconvolve the seismic data, while taking into account the spatial dependency between neighboring traces. The first algorithm employs in each step a modified maximum posterior mode (MPM) algorithm which estimates a reflectivity column from the corresponding observed trace using the estimate of the preceding reflectivity column. The second algorithm takes into account estimates of both the preceding and subsequent columns in the estimation process. Both algorithms are applied to synthetic and real data and demonstrate better results compared to those obtained by a single-channel deconvolution method. Expectedly, the second algorithm which utilizes more information in the estimation process of each reflectivity column is shown to produce better results than the first algorithm.Index Terms-Reflectivity estimation, multichannel deconvolution, seismic signals, Markov Chain Monte Carlo, maximum posterior mode method.

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“…However, Idier and Goussard [15] showed that the a posteriori likelihood p T / , T − , T \ , Q, R |Y can be expressed as:…”

Section: A Deconvolution Schemementioning

confidence: 99%

Section: B Prior Modelmentioning

confidence: 99%

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Multichannel Deconvolution of Seismic Signals Using Statistical MCMC Methods

Ram

Cohen

Raz

2010

IEEE Trans. Signal Process.

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“…Thus, the space of inputs is Suppose that (9) Then it is clear that contains all the nonzero elements of . Hence, the channel output can be expressed in the frequency domain as (10) where (the Fourier transform of ) is called the channel transfer function matrix. We use the shorthand notation to denote the input-output relation of the channel, where is the operator denoting the channel.…”

Section: Stable Samplingmentioning

confidence: 99%

“…In general, the channel inputs have overlapping spectra. Some example applications where MIMO channels arise are multiuser or multiaccess wireless communications and space-time coding with antenna arrays, or telephone digital subscriber loops [1]- [4], multisensor biomedical signals [5], [6], multitrack magnetic recording [7], multiple speaker (or other acoustic source) separation with microphone arrays [8], [9], geophysical data processing [10], and multichannel image restoration [11], [12].…”

mentioning

confidence: 99%

Multiple-Input Multiple-Output Sampling: Necessary Density Conditions

Venkataramani

Bresler

2004

IEEE Trans. Inform. Theory

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Abstract-We consider the problem of multiple-input multipleoutput (MIMO) sampling of multiband signals. In this problem, a set of input signals is passed through a MIMO channel modeled as a known linear time-invariant system. The inputs are modeled as multiband signals whose spectral supports are sets of finite measure and the channel outputs are sampled on nonuniform sampling sets. The aim is to reconstruct the inputs from the output samples. This sampling scheme is quite general and it encompasses various others including Papoulis' generalized sampling and nonuniform sampling as special cases. We introduce notions of joint upper and lower densities for collections of sampling sets and then derive necessary conditions on these densities for stable sampling and consistent reconstruction of the channel inputs from the sampled outputs. These results generalize classical density results for stable sampling and interpolation due to Landau.

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