Wave-equation Rayleigh wave inversion using fundamental and higher modes

Zhendong Zhang, Tariq Ali Alkhalifah

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

4 Scopus citations


Recorded surface waves often provide reasonable estimates of the Shear wave velocity in the near surface. However, these estimates tend to be low in resolution considering that they depend on dispersion nature of the fundamental mode of surface waves. We present a surface-wave inversion method that inverts for the S-wave velocity from the fundamental- and higher-modes of Rayleigh waves. The proposed method aims to maximize the similarity of the phase velocity (f − v) spectrum of the surface waves with all-Rayleigh wave modes (if they exist) in the inversion. The f − v spectrum is calculated using the linear Radon transform and by using a local similarity-based objective function, we do not need to pick velocities in the spectrum plots. Thus, the best match between the predicted and observed data f − v spectrum provides the optimal estimation of S-wave velocity. We derive the gradient of the proposed objective function using the adjoint-state method and solve the optimization problem using the LBFGS method. Our method can invert for lateral velocity variations, include all-mode dispersions, and mitigate the local minimum problem in full waveform inversion with a reasonable computation cost. Results with synthetic and field data illustrate the benefits and limitations of this method.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2018
PublisherSociety of Exploration Geophysicists
Number of pages5
StatePublished - Aug 27 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Jing Li, Sherif Hanafy and Jerry Schuster for providing the field data and helpful discussions. We thank KAUST for its support and specifically the seismic wave analysis group members for their valuable insights. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.


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