NDLI: Bayesian prestack seismic inversion with a self-adaptive Huber-Markov random-field edge protection scheme

Content Provider	Springer Nature Link
Author	Tian, Yu Kun Zhou, Hui Chen, Han Ming Zou, Ya Ming Guan, Shou Jun
Copyright Year	2013
Abstract	Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber-Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.
Starting Page	453
Ending Page	460
Page Count	8
File Format	PDF
ISSN	16727975
Journal	Applied Geophysics
Volume Number	10
Issue Number	4
e-ISSN	19930658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-03-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Huber edge punishment function markov random-field bayesian framework prestack inversion Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Geophysics

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