Gaussian mixture Markov chain Monte Carlo method for linear seismic inversion

By L. D. de Figueiredo, Dario Grana, M. Roisenberg, Bruno B. Rodrigues. Geophysics, 2019

53 citations recorded by the source corpus.

We have developed a Markov chain Monte Carlo (MCMC) method for joint inversion of seismic data for the prediction of facies and elastic properties. The solution of the inverse problem is defined by the Bayesian posterior distribution of the properties of interest. The prior distribution is a Gaussian mixture model, and each component is associated to a potential configuration of the facies sequence along the seismic trace. The low frequency is incorporated by using facies-dependent depositional trend models for the prior means of the elastic properties in each facies. The posterior distribution is also a Gaussian mixture, for which the Gaussian component can be analytically computed. However, due to the high number of components of the mixture, i.e., the large number of facies configurations, the computation of the full posterior distribution is impractical. Our Gaussian mixture MCMC method allows for the calculation of the full posterior distribution by sampling the facies configurations according to the acceptance/rejection probability. The novelty of the method is the use of an MCMC framework with multimodal distributions for the description of the model properties and the facies along the entire seismic trace. Our method is tested on synthetic seismic data, applied to real seismic data, and validated using a well test.

Subfields: Geophysics, Mechanical Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/41c4b5a3c306b4fe9f13559919a9e72414b1378b

How researchers see this paper

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In Geophysics

1. Siwei Yu
2. Jianwei Ma
3. Xiaoqun Zhang

In Mechanical Engineering

1. Jianwei Ma
2. Siwei Yu

In Oceanography

1. Jianwei Ma
2. Siwei Yu

Citations in the corpus

• Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “DL-based uncertainty analysis methods include Monte Carlo dropout (Gal & Ghahramani, 2016), Markov chain Monte Carlo (MCMC) (de Figueiredo et al., 2019), variational inference (Subedar et al., 2019), etc.”