Bayesian Strategies to Assess Uncertainty in Velocity Models

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

Abstract

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.

Citation

Caiado, C. C., Goldstein, M., & Hobbs, R. W. (2012). Bayesian Strategies to Assess Uncertainty in Velocity Models. Bayesian Analysis, 7(1), 211-234. https://doi.org/10.1214/12-ba707

• 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
• DOI: https://doi.org/10.1214/12-ba707
• Keywords: Gaussian Processes, Metropolis-Hastings algorithm, Seismology, Velocity Modelling.
• Public URL: https://durham-repository.worktribe.com/output/1492719
• Publisher URL: http://projecteuclid.org/euclid.ba/1339616730