Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

The Markov chain Monte Carlo (MCMC) method based on Metropolis−Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior information. However, the sampling function of the tradi...

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Bibliographic Details
Main Authors:	Hao Wu, Yingpin Chen, Shu Li, Zhenming Peng
Format:	Article
Language:	English
Published:	MDPI AG 2019-07-01
Series:	Energies
Subjects:	metropolis–hastings sampling bayesian impedance inversion markov chain monte carlo gaussian distribution
Online Access:	https://www.mdpi.com/1996-1073/12/14/2744

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