2015
DOI: 10.1190/geo2014-0461.1
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Multiscale adjoint waveform tomography for surface and body waves

Abstract: International audienceWe have developed a wavelet-multiscale adjoint scheme for the elastic full-waveform inversion of seismic data, including body waves (BWs) and surface waves (SWs). We start the inversion on the SW portion of the seismograms. To avoid cycle skipping and reduce the dependence on the initial model of these dispersive waves, we commence by minimizing an envelope-based misfit function. Subsequently, we proceed to the minimization of a waveform-difference (WD) metric applied to the SWs only. Aft… Show more

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Cited by 96 publications
(30 citation statements)
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References 112 publications
(131 reference statements)
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“…Results with synthetic data showed this to be a robust and efficient method for reconstructing the S-velocity model at the near surface. Another surfacewave inversion strategy is proposed by (Yuan et al, 2015), who developed a wavelet multi-scale adjoint method which combined surface waves and body waves. Synthetic tests showed that this approach can avoid cycle skipping for some models .…”
Section: Introductionmentioning
confidence: 99%
“…Results with synthetic data showed this to be a robust and efficient method for reconstructing the S-velocity model at the near surface. Another surfacewave inversion strategy is proposed by (Yuan et al, 2015), who developed a wavelet multi-scale adjoint method which combined surface waves and body waves. Synthetic tests showed that this approach can avoid cycle skipping for some models .…”
Section: Introductionmentioning
confidence: 99%
“…Inverting surface waves for the S-wave velocity model falls into two categories: (1) the classical method of inverting dispersion curves (Evison et al 1959;Park et al 1998;Xia 2014) for a 1-D layered medium, and (2) full waveform inversion (Groos et al 2014;Solano et al 2014;Dou & Ajo-Franklin 2014;Bohlen et al 2015;Yuan et al 2015) for 2-D and 3-D media. The classical method accurately inverts for a 1-D S-wave velocity model, but becomes less accurate with increasing lateral heterogeneity in the subsurface.…”
Section: Introductionmentioning
confidence: 99%
“…However, depending on the tomographic resolution and data quality, the biases may be larger than the error bars. A qualitative look at the detrimental effects that incorrect density information has on the wave speed models has recently been shown by Yuan et al (2015).…”
Section: Velocity Bias Estimationmentioning
confidence: 99%