Bayesian Analysis

Bayesian Strategies to Assess Uncertainty in Velocity Models

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

Full-text: Open access

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.

Article information

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

Dates
First available in Project Euclid: 13 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1339616730

Digital Object Identifier
doi:10.1214/12-BA707

Mathematical Reviews number (MathSciNet)
MR2896717

Zentralblatt MATH identifier
1330.62447

Keywords
Gaussian Processes Metropolis-Hastings algorithm Seismology Velocity Modelling

Citation

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. https://projecteuclid.org/euclid.ba/1339616730


Export citation