Born reflection kernel analysis and wave-equation reflection traveltime inversion in elastic media

Tengfei Wang, Jiubing Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Elastic reflection waveform inversion (ERWI) utilize the reflections to update the low and intermediate wavenumbers in the deeper part of model. However, ERWI suffers from the cycle-skipping problem due to the objective function of waveform residual. Since traveltime information relates to the background model more linearly, we use the traveltime residuals as objective function to update background velocity model using wave equation reflected traveltime inversion (WERTI). The reflection kernel analysis shows that mode decomposition can suppress the artifacts in gradient calculation. We design a two-step inversion strategy, in which PP reflections are firstly used to invert P wave velocity (Vp), followed by S wave velocity (Vs) inversion with PS reflections. P/S separation of multi-component seismograms and spatial wave mode decomposition can reduce the nonlinearity of inversion effectively by selecting suitable P or S wave subsets for hierarchical inversion. Numerical example of Sigsbee2A model validates the effectiveness of the algorithms and strategies for elastic WERTI (E-WERTI).
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2017
PublisherSociety of Exploration Geophysicists
StatePublished - Aug 17 2017
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work is supported by the National Natural Science Foundation of China (NO.41474099, 41674117 & 41630964). This paper is also based upon the work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award NO. 2230. We appreciate the open-source package of DENISE from and Mines Java Toolkit from We thank the useful advice from Tariq Alkhalifah (KAUST), Qiang Guo (KAUST), Zedong Wu (KAUST), Chenlong Wang (Tongji University) and Benxin Chi (Los Alamos).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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