Tomographic wavelet estimation via higher order statistics

Georgios B. Giannakis; Jerry M. Mendel

doi:10.1190/1.1893020

Tomographic wavelet estimation via higher order statistics

Georgios B. Giannakis
, Jerry M. Mendel

Research output: Contribution to conference › Paper › peer-review

Abstract

Efficient estimation of nonminimum-phase (NMP) ARMA wavelets is a very important part of seismic deconvolution. "Phase-blind," autocorrelation-based approaches, used up to now, are restricted to minimum-phase (MP) wavelets. In this paper we develop two methods for NMP wavelet identification. We model the reflectivity sequence as a non-Gaussian, stationary and independent random process (rp). The estimated wavelet best fits the amplitude sensitive autocorrelation sequence and a phase-sensitive higher order statistics sequence of the (perhaps noisy) seismogram. The parameter estimates are consistent and robust with respect to additive Gaussian white noise. We test our methods and compare them with existing NMP wavelet estimation techniques, using synthetic examples.

Original language	English (US)
Pages	514-516
Number of pages	3
DOIs	https://doi.org/10.1190/1.1893020
State	Published - 1986
Externally published	Yes
Event	1986 Society of Exploration Geophysicists Annual Meeting, SEG 1986 - Houston, United States Duration: Nov 2 1986 → Nov 6 1986

Conference

Conference	1986 Society of Exploration Geophysicists Annual Meeting, SEG 1986
Country/Territory	United States
City	Houston
Period	11/2/86 → 11/6/86

Bibliographical note

Publisher Copyright:
© 1986 SEG Annual Meeting, SEG 1986. All rights reserved.

Publisher link

10.1190/1.1893020

Find It

Find It at U of M Libraries

Cite this

@conference{ec8680edad554903bc846bf31892e4ae,

title = "Tomographic wavelet estimation via higher order statistics",

abstract = "Efficient estimation of nonminimum-phase (NMP) ARMA wavelets is a very important part of seismic deconvolution. {"}Phase-blind,{"} autocorrelation-based approaches, used up to now, are restricted to minimum-phase (MP) wavelets. In this paper we develop two methods for NMP wavelet identification. We model the reflectivity sequence as a non-Gaussian, stationary and independent random process (rp). The estimated wavelet best fits the amplitude sensitive autocorrelation sequence and a phase-sensitive higher order statistics sequence of the (perhaps noisy) seismogram. The parameter estimates are consistent and robust with respect to additive Gaussian white noise. We test our methods and compare them with existing NMP wavelet estimation techniques, using synthetic examples.",

author = "Giannakis, \{Georgios B.\} and Mendel, \{Jerry M.\}",

note = "Publisher Copyright: {\textcopyright} 1986 SEG Annual Meeting, SEG 1986. All rights reserved.; 1986 Society of Exploration Geophysicists Annual Meeting, SEG 1986 ; Conference date: 02-11-1986 Through 06-11-1986",

year = "1986",

doi = "10.1190/1.1893020",

language = "English (US)",

pages = "514--516",

}

TY - CONF

T1 - Tomographic wavelet estimation via higher order statistics

AU - Giannakis, Georgios B.

AU - Mendel, Jerry M.

PY - 1986

Y1 - 1986

N2 - Efficient estimation of nonminimum-phase (NMP) ARMA wavelets is a very important part of seismic deconvolution. "Phase-blind," autocorrelation-based approaches, used up to now, are restricted to minimum-phase (MP) wavelets. In this paper we develop two methods for NMP wavelet identification. We model the reflectivity sequence as a non-Gaussian, stationary and independent random process (rp). The estimated wavelet best fits the amplitude sensitive autocorrelation sequence and a phase-sensitive higher order statistics sequence of the (perhaps noisy) seismogram. The parameter estimates are consistent and robust with respect to additive Gaussian white noise. We test our methods and compare them with existing NMP wavelet estimation techniques, using synthetic examples.

AB - Efficient estimation of nonminimum-phase (NMP) ARMA wavelets is a very important part of seismic deconvolution. "Phase-blind," autocorrelation-based approaches, used up to now, are restricted to minimum-phase (MP) wavelets. In this paper we develop two methods for NMP wavelet identification. We model the reflectivity sequence as a non-Gaussian, stationary and independent random process (rp). The estimated wavelet best fits the amplitude sensitive autocorrelation sequence and a phase-sensitive higher order statistics sequence of the (perhaps noisy) seismogram. The parameter estimates are consistent and robust with respect to additive Gaussian white noise. We test our methods and compare them with existing NMP wavelet estimation techniques, using synthetic examples.

UR - https://www.scopus.com/pages/publications/85057106603

UR - https://www.scopus.com/pages/publications/85057106603#tab=citedBy

U2 - 10.1190/1.1893020

DO - 10.1190/1.1893020

M3 - Paper

AN - SCOPUS:85057106603

SP - 514

EP - 516

T2 - 1986 Society of Exploration Geophysicists Annual Meeting, SEG 1986

Y2 - 2 November 1986 through 6 November 1986

ER -

Tomographic wavelet estimation via higher order statistics

Abstract

Conference

Bibliographical note

Publisher link

Find It

Other files and links

Fingerprint

Cite this