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Bayesian Strategies to Assess Uncertainty in Velocity Models

Caiado, Camila C.S.; Goldstein, Michael; Hobbs, Richard W.

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Richard W. Hobbs


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.


Caiado, C. C., Goldstein, M., & Hobbs, R. W. (2012). Bayesian Strategies to Assess Uncertainty in Velocity Models. Bayesian Analysis, 7(1), 211-234.

Journal Article Type Article
Publication Date Mar 1, 2012
Deposit Date Nov 27, 2012
Publicly Available Date Feb 16, 2016
Journal Bayesian Analysis
Print ISSN 1936-0975
Electronic ISSN 1931-6690
Publisher International Society for Bayesian Analysis (ISBA)
Peer Reviewed Peer Reviewed
Volume 7
Issue 1
Pages 211-234
Keywords Gaussian Processes, Metropolis-Hastings algorithm, Seismology, Velocity Modelling.
Publisher URL


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