Abstract
Full waveform inversion (FWI) is a rapidly developed inversion method in recent years, and the resolution is much higher than traditional inversion methods, such as tomography and migration velocity analysis. However, FWI is much dependent on starting model and low frequency information, and easy to fall into local minima because of cycle skipping issue. We proposed Curvelet transform multi-scale strategy to improve the effect of bad starting model by selecting different scale information of observed data during inversion. Test of frequency domain FWI on Marmousi model shows that the result of inversion has a better stability and convergence with Curvelet transform multi-scale method than traditional methods.
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