Regional tomographic inversion of the amplitude and phase of Rayleigh waves with 2-D sensitivity kernels

Yingjie Yang*, Donald W. Forsyth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


In this study, we test the adequacy of 2-D sensitivity kernels for fundamental-mode Rayleigh waves based on the single-scattering (Born) approximation to account for the effects of heterogeneous structure on the wavefield in a regional surface wave study. The calculated phase and amplitude data using the 2-D sensitivity kernels are compared to phase and amplitude data obtained from seismic waveforms synthesized by the pseudo-spectral method for plane Rayleigh waves propagating through heterogeneous structure. We find that the kernels can accurately predict the perturbation of the wavefield even when the size of anomaly is larger than one wavelength. The only exception is a systematic bias in the amplitude within the anomaly itself due to a site response. An inversion method of surface wave tomography based on the sensitivity kernels is developed and applied to synthesized data obtained from a numerical simulation modelling Rayleigh wave propagation over checkerboard structure. By comparing recovered images to input structure, we illustrate that the method can almost completely recover anomalies within an array of stations when the size of the anomalies is larger than or close to one wavelength of the surface waves. Surface wave amplitude contains important information about Earth structure and should be inverted together with phase data in surface wave tomography.

Original languageEnglish
Pages (from-to)1148-1160
Number of pages13
JournalGeophysical Journal International
Issue number3
Publication statusPublished - Sept 2006
Externally publishedYes


  • Sensitivity kernels
  • Surface waves
  • Tomography


Dive into the research topics of 'Regional tomographic inversion of the amplitude and phase of Rayleigh waves with 2-D sensitivity kernels'. Together they form a unique fingerprint.

Cite this