Multichannel Semi-blind Sparse Deconvolution of Seismic Signals

Merabi Mirel; Israel Cohen; Anthony A. Vassiliou

doi:https://doi.org/10.1190/segam2017-17681178.1

Multichannel Semi-blind Sparse Deconvolution of Seismic Signals

Merabi Mirel, Israel Cohen, Anthony A. Vassiliou

Electrical and Computer Engineering

Technion - Israel Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

Seismic deconvolution is associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we address the problem of multichannel semi-blind seismic deconvolution, where the wavelet is unknown and there is some uncertainty in the assumed wavelet. We present a novel, two-stage iterative algorithm that recovers both the reflectivity and the wavelet. While the reflectivity series is recovered using sparse modeling of the signal, the wavelet is recovered using l₂ minimization, exploiting the fact that all channels share the same wavelet.

Original language	English
Pages (from-to)	514-518
Number of pages	5
Journal	SEG Technical Program Expanded Abstracts
DOIs	https://doi.org/10.1190/segam2017-17681178.1
State	Published - 17 Aug 2017
Event	Society of Exploration Geophysicists International Exposition and 87th Annual Meeting, SEG 2017 - Houston, United States Duration: 24 Sep 2017 → 29 Sep 2017

All Science Journal Classification (ASJC) codes

Geotechnical Engineering and Engineering Geology
Geophysics

Access to Document

https://doi.org/10.1190/segam2017-17681178.1

Cite this

@article{4e793c11c4e34d8e91b4b741c0571ce5,

title = "Multichannel Semi-blind Sparse Deconvolution of Seismic Signals",

abstract = "Seismic deconvolution is associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we address the problem of multichannel semi-blind seismic deconvolution, where the wavelet is unknown and there is some uncertainty in the assumed wavelet. We present a novel, two-stage iterative algorithm that recovers both the reflectivity and the wavelet. While the reflectivity series is recovered using sparse modeling of the signal, the wavelet is recovered using l2 minimization, exploiting the fact that all channels share the same wavelet.",

author = "Merabi Mirel and Israel Cohen and Vassiliou, {Anthony A.}",

note = "Publisher Copyright: {\textcopyright} 2017 SEG.; Society of Exploration Geophysicists International Exposition and 87th Annual Meeting, SEG 2017 ; Conference date: 24-09-2017 Through 29-09-2017",

year = "2017",

month = aug,

day = "17",

doi = "https://doi.org/10.1190/segam2017-17681178.1",

language = "الإنجليزيّة",

pages = "514--518",

journal = "SEG Technical Program Expanded Abstracts",

issn = "1052-3812",

publisher = "Society of Exploration Geophysicists",

}

TY - JOUR

T1 - Multichannel Semi-blind Sparse Deconvolution of Seismic Signals

AU - Mirel, Merabi

AU - Cohen, Israel

AU - Vassiliou, Anthony A.

PY - 2017/8/17

Y1 - 2017/8/17

N2 - Seismic deconvolution is associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we address the problem of multichannel semi-blind seismic deconvolution, where the wavelet is unknown and there is some uncertainty in the assumed wavelet. We present a novel, two-stage iterative algorithm that recovers both the reflectivity and the wavelet. While the reflectivity series is recovered using sparse modeling of the signal, the wavelet is recovered using l2 minimization, exploiting the fact that all channels share the same wavelet.

AB - Seismic deconvolution is associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we address the problem of multichannel semi-blind seismic deconvolution, where the wavelet is unknown and there is some uncertainty in the assumed wavelet. We present a novel, two-stage iterative algorithm that recovers both the reflectivity and the wavelet. While the reflectivity series is recovered using sparse modeling of the signal, the wavelet is recovered using l2 minimization, exploiting the fact that all channels share the same wavelet.

UR - http://www.scopus.com/inward/record.url?scp=85121858040&partnerID=8YFLogxK

U2 - https://doi.org/10.1190/segam2017-17681178.1

DO - https://doi.org/10.1190/segam2017-17681178.1

M3 - مقالة من مؤنمر

SN - 1052-3812

SP - 514

EP - 518

JO - SEG Technical Program Expanded Abstracts

JF - SEG Technical Program Expanded Abstracts

T2 - Society of Exploration Geophysicists International Exposition and 87th Annual Meeting, SEG 2017

Y2 - 24 September 2017 through 29 September 2017

ER -

Multichannel Semi-blind Sparse Deconvolution of Seismic Signals

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this