Abstract
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 language | English (US) |
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Title of host publication | EAGE 2020 Annual Conference & Exhibition Online |
Publisher | European Association of Geoscientists & Engineers |
DOIs | |
State | Published - 2020 |
Bibliographical note
KAUST Repository Item: Exported on 2021-03-25Acknowledgements: 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.