Rayleigh Wave Dispersion Spectrum Inversion Across Scales

Zhendong Zhang, Erdinc Saygin, Leiyu He, Tariq Ali Alkhalifah

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Traditional approaches of using dispersion curves for S-wave velocity reconstruction have limitations, principally, the 1D-layered model assumption and the automatic/manual picking of dispersion curves. At the same time, conventional full-waveform inversion (FWI) can easily converge to a non-global minimum when applied directly to complicated surface waves. Alternatively, the recently introduced wave equation dispersion spectrum inversion method can avoid these limitations, by applying the adjoint state method on the dispersion spectra of the observed and predicted data and utilizing the local similarity objective function to depress cycle skipping. We apply the wave equation dispersion spectrum inversion to three real datasets of different scales: tens of meters scale active-source data for estimating shallow targets, tens of kilometers scale ambient noise data for reservoir characterization and a continental-scale seismic array data for imaging the crust and uppermost mantle. We use these three open datasets from exploration to crustal scale seismology to demonstrate the effectiveness of the inversion method. The dispersion spectrum inversion method adapts well to the different-scale data without any special tuning. The main benefits of the proposed method over traditional methods are that (1) it can handle lateral variations; (2) it avoids direct picking dispersion curves; (3) it utilizes both the fundamental and higher modes of Rayleigh waves, and (4) the inversion can be solved using gradient-based local optimizations. Compared to the conventional 1D inversion, the dispersion spectrum inversion requires more computational cost since it requires solving the 2D/3D elastic wave equation in each iteration. 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)
JournalSurveys in Geophysics
DOIs
StatePublished - Oct 19 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-10-21
Acknowledgements: We appreciate Caroline Johnson’s help in improving the manuscript. E. Saygin and L. He wish to acknowledge financial assistance provided through Australian National Low Emissions Coal Research and Development (ANLEC R&D). E. Saygin was supported by CSIRO’s Deep Earth Imaging Future Science Platform. L. He was supported by China Scholarship Council. ANLEC R&D is supported by Australian Coal Association Low Emissions Technology Limited and the Australian Government through the Clean Energy Initiative. We thank the InterPACIFIC project for providing the field data (data available here: http://interpacific.geopsy.org/). We thank David Lumley for his contribution to the retrieval of the continuous part of the SW HUB dataset (data available here: https://wapims.dmp.wa.gov.au/WAPIMS/Search/SwHubCarbonStorage). We thank Weisen Shen for providing the reference S-wave velocity model (data available here: http://ciei.colorado.edu/Models/). We extend our thanks to Roman Pevzner for providing the reference S-wave velocity of Harvey-1 well. Z. Zhang and T. Alkhalifah 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 and Technology (KAUST) in Thuwal, Saudi Arabia.

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics

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