Bayesian strategies to assess uncertainty in velocity models. (English) Zbl 1330.62447

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

Bayesian Anal. 7, No. 1, 211-234 (2012).

Summary: 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.

MSC:

62P35	Applications of statistics to physics
60J22	Computational methods in Markov chains
62F15	Bayesian inference
86A15	Seismology (including tsunami modeling), earthquakes
86A32	Geostatistics

Keywords:

Gaussian processes; Metropolis-Hastings algorithm; seismology; velocity modelling