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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings

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.
Original languageEnglish
Title of host publicationSEG International Exposition and Annual Meeting, 24-29 September, Houston, Texas
Number of pages4
Place of PublicationHouston, Texas
Publication date24 Aug 2017
Publication statusPublished - 24 Aug 2017

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