Publication details for Camila CaiadoCaiado, Camila C. S., Goldstein, Michael & Hobbs, Richard W. (2012). Bayesian Strategies to Assess Uncertainty in Velocity Models. Bayesian Analysis 7(1): 211-234.
- Publication type: Journal Article
- ISSN/ISBN: 1936-0975, 1931-6690
- DOI: 10.1214/12-BA707
- Keywords: Gaussian Processes, Metropolis-Hastings algorithm, Seismology, Velocity Modelling.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.