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

Rasmus Bødker Madsen, Andrea Zunino, Thomas Mejer Hansen

Publikation: Bidrag til bog/rapport/konferenceproceedingsKonferenceartikel i proceedingspeer review

7 Citationer (Scopus)

Abstrakt

A realistic noise model is essential for trustworthy inversion of geophysical data. Sometimes, as in case of seismic data, quantification of the noise model is non-trivial. To remedy this, a hierarchical Bayes approach can be adopted in which properties 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 reflection 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.
OriginalsprogEngelsk
TitelSEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas
ForlagSociety of Exploration Geophysicists
Sider601-606
Antal sider6
DOI
StatusUdgivet - 17 aug. 2017
Udgivet eksterntJa
BegivenhedSociety of Exploration Geophysicists International Exposition and 87th Annual Meeting - George R. Brown Convention Center, Houston, USA
Varighed: 24 sep. 201729 sep. 2017
Konferencens nummer: 87

Publikationsserier

NavnSEG Technical Program Expanded Abstracts
ForlagSociety of Exploration Geophysicists
Vol/bind2017
ISSN (Trykt)1052-3812
ISSN (Elektronisk)1949-4645

Konference

KonferenceSociety of Exploration Geophysicists International Exposition and 87th Annual Meeting
Forkortet titelSEG 2017
Land/OmrådeUSA
ByHouston
Periode24/09/1729/09/17

Programområde

  • Programområde 3: Energiressourcer

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