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March 2012 Bayesian Strategies to Assess Uncertainty in Velocity Models
Camila C. S. Caiado, Michael Goldstein, Richard W. Hobbs
Bayesian Anal. 7(1): 211-234 (March 2012). DOI: 10.1214/12-BA707


Quantifying uncertainty in models derived from observed seismic data is a major issue. In this research we examine the geological structure of the sub-surface using controlled source seismology which gives the data in time and the distance between the acoustic source and the receiver. Inversion tools exist to map these data into a depth model, but a full exploration of the uncertainty of the model is rarely done because robust strategies do not exist for large non-linear complex systems. There are two principal sources of uncertainty: the first comes from the input data which is noisy and band-limited; the second is from the model parameterisation and forward algorithm which approximate the physics to make the problem tractable. To address these issues we propose a Bayesian approach using the Metropolis-Hastings algorithm.


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Camila C. S. Caiado. Michael Goldstein. Richard W. Hobbs. "Bayesian Strategies to Assess Uncertainty in Velocity Models." Bayesian Anal. 7 (1) 211 - 234, March 2012.


Published: March 2012
First available in Project Euclid: 13 June 2012

zbMATH: 1330.62447
MathSciNet: MR2896717
Digital Object Identifier: 10.1214/12-BA707

Keywords: Gaussian processes , Metropolis-Hastings algorithm , seismology , Velocity Modelling

Rights: Copyright © 2012 International Society for Bayesian Analysis


Vol.7 • No. 1 • March 2012
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