On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes. / Madsen, Rasmus Bødker; Zunino, Andrea; Hansen, Thomas Mejer.

SEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas . Houston, Texas, 2017.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Madsen, RB, Zunino, A & Hansen, TM 2017, On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes. i SEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas . Houston, Texas.

APA

Madsen, R. B., Zunino, A., & Hansen, T. M. (2017). On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes. I SEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas

Vancouver

Madsen RB, Zunino A, Hansen TM. On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes. I SEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas . Houston, Texas. 2017

Author

Madsen, Rasmus Bødker ; Zunino, Andrea ; Hansen, Thomas Mejer. / On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes. SEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas . Houston, Texas, 2017.

Bibtex

@inproceedings{07271b8e6b22446fb9af8e1f3b3053f6,
title = "On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes",
abstract = "A realistic noise model is essential for trustworthy inversion of geophysical data. Sometimes, as in case of seismic data, quan- tification of the noise model is non-trivial. To remedy this, a hierarchical Bayes approach can be adopted in which proper- ties of the noise model, such as the amplitude of an assumed uncorrelated Gaussian noise model, can be inferred as part of the inversion. Here we demonstrate how such an approach can lead to substantial overfitting of noise when inverting a 1D re- flection seismic NMO data set. We then argue that usually the noise model is correlated, and suggest to infer the amplitude of a correlated Gaussian noise model. This provides better results than assuming an uncorrelated model. In general though, the results suggest that care should be taken using the hierarchical Bayes approach to infer the noise model.",
author = "Madsen, {Rasmus B{\o}dker} and Andrea Zunino and Hansen, {Thomas Mejer}",
year = "2017",
month = aug,
day = "24",
language = "English",
booktitle = "SEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas",

}

RIS

TY - GEN

T1 - On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes

AU - Madsen, Rasmus Bødker

AU - Zunino, Andrea

AU - Hansen, Thomas Mejer

PY - 2017/8/24

Y1 - 2017/8/24

N2 - A realistic noise model is essential for trustworthy inversion of geophysical data. Sometimes, as in case of seismic data, quan- tification of the noise model is non-trivial. To remedy this, a hierarchical Bayes approach can be adopted in which proper- ties of the noise model, such as the amplitude of an assumed uncorrelated Gaussian noise model, can be inferred as part of the inversion. Here we demonstrate how such an approach can lead to substantial overfitting of noise when inverting a 1D re- flection seismic NMO data set. We then argue that usually the noise model is correlated, and suggest to infer the amplitude of a correlated Gaussian noise model. This provides better results than assuming an uncorrelated model. In general though, the results suggest that care should be taken using the hierarchical Bayes approach to infer the noise model.

AB - A realistic noise model is essential for trustworthy inversion of geophysical data. Sometimes, as in case of seismic data, quan- tification of the noise model is non-trivial. To remedy this, a hierarchical Bayes approach can be adopted in which proper- ties of the noise model, such as the amplitude of an assumed uncorrelated Gaussian noise model, can be inferred as part of the inversion. Here we demonstrate how such an approach can lead to substantial overfitting of noise when inverting a 1D re- flection seismic NMO data set. We then argue that usually the noise model is correlated, and suggest to infer the amplitude of a correlated Gaussian noise model. This provides better results than assuming an uncorrelated model. In general though, the results suggest that care should be taken using the hierarchical Bayes approach to infer the noise model.

UR - https://www.onepetro.org/conference-paper/SEG-2017-17725822

M3 - Article in proceedings

BT - SEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas

CY - Houston, Texas

ER -

ID: 185407570