Stats4Grads: Bayesian strategies to assess uncertainty in velocity models
19 May 2010 14:15 in CM 221
Quantifying uncertainty in models derived from observed seismic data is a major issue. In this project, we examine the geological structure of the subsurface 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, and more sinister, is from the model parameterisation and forward algorithm themselves, which approximate to the physics to make the problem tractable. To address these issues we propose a Bayesian approach using the Metropolis-Hastings algorithm.
See the Stats4Grads page for more details about this series.