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

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
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