Fast uncertainty quantification for 2D full-waveform inversion with randomized source subsampling

TitleFast uncertainty quantification for {2D} full-waveform inversion with randomized source subsampling
Publication TypeConference
Year of Publication2014
AuthorsZhilong Fang, Curt Da Silva, Felix J. Herrmann
Conference NameEAGE Annual Conference Proceedings
KeywordsEAGE, FWI, Markov chain Monte Carlo, randomized sources subsampling, Uncertainty quantification

Uncertainties arise in every area of seismic exploration, especially in full-waveform inversion, which is highly non-linear. In the framework of Bayesian inference, uncertainties can be analyzed by sampling the posterior probability density distribution with a Markov chain Monte-Carlo (McMC) method. We reduce the cost of computing the posterior distribution by working with randomized subsets of sources. These approximations, together with the Gaussian assumption and approximation of the Hessian, leads to a computational tractable uncertainty quantification. Application of this approach to a synthetic leads to standard deviations and confidence intervals that are qualitatively consistent with our expectations.


Citation Keyfang2014EAGEfuq
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