Bayesian Analysis

Bayesian Strategies to Assess Uncertainty in Velocity Models

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

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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.

Article information

Bayesian Anal., Volume 7, Number 1 (2012), 211-234.

First available in Project Euclid: 13 June 2012

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Zentralblatt MATH identifier

Gaussian Processes Metropolis-Hastings algorithm Seismology Velocity Modelling


Caiado, Camila C. S.; Hobbs, Richard W.; Goldstein, Michael. Bayesian Strategies to Assess Uncertainty in Velocity Models. Bayesian Anal. 7 (2012), no. 1, 211--234. doi:10.1214/12-BA707.

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