Caiado, Camila C. S. and Goldstein, Michael and Hobbs, Richard W. (2012) 'Bayesian strategies to assess uncertainty in velocity models.', Bayesian analysis., 7 (1). pp. 211-234.
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.
|Keywords:||Gaussian Processes, Metropolis-Hastings algorithm, Seismology, Velocity Modelling.|
|Full text:||(VoR) Version of Record|
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|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||http://projecteuclid.org/euclid.ba/1339616730|
|Date accepted:||No date available|
|Date deposited:||16 February 2016|
|Date of first online publication:||March 2012|
|Date first made open access:||No date available|
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