Rayleigh Wave Phase-Slope Tomography

Z. Zhang, Tariq Ali Alkhalifah, E. Saygin, L. He

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


Traditional approaches of utilizing the dispersion curves in S-wave velocity reconstruction have many limitations, namely, the 1D layered model assumption and the automatic/manual picking of dispersion curves. On the other hand, conventional full-waveform inversion (FWI) can easily converge to one of the local minima when applied directly to complicated surface waves. Alternatively, a wave equation dispersion spectrum inversion can avoid these limitations, by inverting the slopes of arrivals at different frequencies. A local-similarity objective function is used to avoid possible cycle skipping. We apply the proposed method on the large-scale ambient-noise data recorded at a large-N array with over 3000 recorders. So we can estimate the shear-wave velocities down to 1.8 km depth. The main benefits of the proposed method are 1) it handles lateral variations; 2) it avoids picking dispersion curves; 3) it utilizes both the fundamental- and higher-modes of Rayleigh waves, and 4) it can be solved using gradientbased local optimizations. A good match between the observed and predicted dispersion spectra also leads to a reasonably good match between the observed and predicted waveforms, though the inversion does not aim to match the waveforms.
Original languageEnglish (US)
Title of host publicationEAGE 2020 Annual Conference & Exhibition Online
PublisherEuropean Association of Geoscientists & Engineers
StatePublished - 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-03-25
Acknowledgements: The authors wish to acknowledge the financial assistance provided through Australian National Low Emissions Coal Research and Development (ANLEC R&D). We thank David Lumley for his contribution to the retrieval of the continuous part of the SW HUB dataset. 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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