Reflection intensity waveform inversion

Yike Liu, Bin He, Zhendong Zhang, Yingcai Zheng, Peng Li

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Traditional iteration-based full-waveform inversion (FWI) methods encounter serious challenges if the initial velocity model is far from the true model or if the observed data are lacking low-frequency content. As such, the optimization algorithm may be trapped in local minima and fail to go to a global optimal model. In addition, the traditional FWI method requires long-offset data to update the deep structure of a velocity model with diving waves. To overcome the disadvantages of traditional FWI under these circumstances, we have developed a reflection intensity waveform inversion method. This method aims to minimize the seismic intensity differences between the modeled reflection data and field data. Our method is less dependent on the starting model, and long-offset data are no longer required. The wave intensity, proportional to the square of the original data amplitude, can have a low-frequency band and a higher frequency band, even for waveforms without initial low-frequency content. Our multiscale intensity inversion starts from the low-frequency information in the intensity data, and it can largely avoid the cycle-skipping problem. Synthetic and field data examples demonstrate that our method is able to overcome cycle skipping in handling data with no low-frequency information.
Original languageEnglish (US)
Pages (from-to)R263-R273
Number of pages1
Issue number3
StatePublished - Apr 8 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-02-16
Acknowledgements: We would like to express our sincere gratitude to S. Xu and H. Zhou for their insightful discussions. We also thank the associate editor T. Alkhalifah, editor-in-chief J. Shragge, assistant editor A. Cheng, and four anonymous reviewers for the constructive comments. The research was partially funded by the National Nature Science Foundation of China (grant nos. 41730425 and 41430321) and IGGCAS (grant no. 2019031).


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