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Durham University

Department of Earth Sciences

Profile

Publication details for Professor Richard Hobbs

Caiado, Camila C. S., Goldstein, Michael & Hobbs, Richard W. (2012). Bayesian Strategies to Assess Uncertainty in Velocity Models. Bayesian Analysis 7(1): 211-234.

Author(s) from Durham

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.