Robust Full-Waveform Inversion with Radon-Domain Matching Filter

Bingbing Sun, Tariq Ali Alkhalifah

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


"Cycle skipping" is a severe issue in Full-Waveform Inversion.One option to overcome it is to extend the search space to allow data comparisons beyond the "point-to-point" subtraction.A matching filter can be computed by deconvolving the measured data from the predicted ones. If the model is correct, the resulting matching filter would be a Dirac delta function in which the energy is focused at zero lag. An optimization problem can be formulated by penalizing this matching filter departure from a Dirac delta function. As the matching filter replaces the local, sample-by-sample, comparison with a global one using deconvolution, it can reduce the “cycle skipping” problem. As the matching filter is computed using the whole trace of the measured and predicted data, it is prone to unwanted cross-talks of different events.We propose to perform the deconvolution in the Radon domain to reduce those kinds of cross-talks and improve the inversion. We first transform both the measured and the predicted data into the τ - p domain using the local Radon transform. We then perform deconvolution for the trace indexed by the same slope value. The main objective of the proposal is to use the slope information embedded in the Radon transform representation to separate the events and reduce the cross-talks in the deconvolution step. As a result, the objective function tends to be more convex and stabilizes the inversion process.The result obtained for the modified Marmousi model demonstrates the proposed Radon-domain matching-filter approach can converge to a meaningful model given data without the low frequencies below 3 Hz and a v(z) initial model.Compared to the conventional time-space matching-filter approach, the Radon-domain approach shows fewer artifacts in the model and better fitting of the measured data.The result corresponding to the Chevron 2014 benchmark dataset also shows the good performance of the proposed approach.
Original languageEnglish (US)
Pages (from-to)R707-R724
Number of pages1
Issue number5
StatePublished - May 14 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank the associate editor S. Operto and the assistant editor D. Draganov for their constructive comments that helped to improve the paper. The authors also thank the SWAG group for useful discussion and the resources of the SHAHEEN supercomputing laboratory at KAUST. We appreciate Chevron for the 2014 FWI benchmark dataset.


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