Feasibility of waveform inversion of Rayleigh waves for shallow shear-wave velocity using a genetic algorithm

Journal of Applied Geophysics
By: , and 

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Abstract

Conventional surface wave inversion for shallow shear (S)-wave velocity relies on the generation of dispersion curves of Rayleigh waves. This constrains the method to only laterally homogeneous (or very smooth laterally heterogeneous) earth models. Waveform inversion directly fits waveforms on seismograms, hence, does not have such a limitation. Waveforms of Rayleigh waves are highly related to S-wave velocities. By inverting the waveforms of Rayleigh waves on a near-surface seismogram, shallow S-wave velocities can be estimated for earth models with strong lateral heterogeneity. We employ genetic algorithm (GA) to perform waveform inversion of Rayleigh waves for S-wave velocities. The forward problem is solved by finite-difference modeling in the time domain. The model space is updated by generating offspring models using GA. Final solutions can be found through an iterative waveform-fitting scheme. Inversions based on synthetic records show that the S-wave velocities can be recovered successfully with errors no more than 10% for several typical near-surface earth models. For layered earth models, the proposed method can generate one-dimensional S-wave velocity profiles without the knowledge of initial models. For earth models containing lateral heterogeneity in which case conventional dispersion-curve-based inversion methods are challenging, it is feasible to produce high-resolution S-wave velocity sections by GA waveform inversion with appropriate priori information. The synthetic tests indicate that the GA waveform inversion of Rayleigh waves has the great potential for shallow S-wave velocity imaging with the existence of strong lateral heterogeneity. ?? 2011 Elsevier B.V.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Feasibility of waveform inversion of Rayleigh waves for shallow shear-wave velocity using a genetic algorithm
Series title Journal of Applied Geophysics
DOI 10.1016/j.jappgeo.2011.09.028
Volume 75
Issue 4
Year Published 2011
Language English
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Journal of Applied Geophysics
First page 648
Last page 655