Adaptive particle swarm optimization assisted MCMC for stochastic inversion

K. Xiang*, L. Han

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


MCMC is widely applied in Bayesian inversion theorem. The advantage of Markov Chain Monte Carlo (MCMC) is that it provides a stochastic optimization approach. The unknown parameters of impedance inversion are extremely high dimensional, ill-posed and non-uniqueness. Global optimization is one of the approaches to update optimization system and find the global minimum, which heavily depends on optimization algorithms. Compared with typical metaheuristic optimization methods, adaptive particle swarm optimization (APSO) has the ability to improve efficiency and can be applied in stochastic inversion issues. We propose APSO assisted MCMC method to solve seismic inversion and output the global optimum solutions. Besides, we test the algorithm on prestack data from the North Sea and compare with the calibrated well-log data, which proves that this method has a high performance on seismic inversion issue.

Original languageEnglish
Title of host publication81st EAGE Conference and Exhibition 2019
PublisherEAGE Publishing BV
ISBN (Electronic)9789462822894
StatePublished - 3 Jun 2019
Event81st EAGE Conference and Exhibition 2019 - London, United Kingdom
Duration: 3 Jun 20196 Jun 2019

Publication series

Name81st EAGE Conference and Exhibition 2019


Conference81st EAGE Conference and Exhibition 2019
Country/TerritoryUnited Kingdom


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