Bayes Meets Tikhonov: Understanding Uncertainty Within Gaussian Framework for Seismic Inversion
Name:
Bayes_meets_Tikhonov__Understanding_Uncertainty_within_Gaussian_Framework_for_Seismic_Inversion.pdf
Size:
1.027Mb
Format:
PDF
Description:
Accepted manuscript
Type
Book Chapter
KAUST Department
Earth Science and Engineering ProgramPhysical Science and Engineering (PSE) Division
Extreme Computing Research Center
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2020-10-22Online Publication Date
2020-10-22Print Publication Date
2021Permanent link to this record
http://hdl.handle.net/10754/665903Metadata
Show full item recordAbstract
In this chapter, we demonstrate the sound connection between the Bayesian approach and the Tikhonov regularisation within Gaussian framework. We provide a thorough uncertainty analysis to answer the following two fundamental questions: (1) How well is the estimate determined by a posteriori PDF, i.e. by the combination of observed data and a priori information? (2) What are the respective contributions of observed data and a priori information? To support the proposed methodology, we demonstrate it through numerical applications in seismic inversions.Citation
Izzatullah, M., Peter, D., Kabanikhin, S., & Shishlenin, M. (2020). Bayes Meets Tikhonov: Understanding Uncertainty Within Gaussian Framework for Seismic Inversion. Studies in Systems, Decision and Control, 121–145. doi:10.1007/978-981-15-8606-4_8Publisher
Springer NatureISBN
97898115860579789811586064