Abstract：Full waveform inversion (FWI) depends on low-frequency information which is always absent in seismic data.To ensure that FWI can steadily converge in the condition of lack of low-frequency information,we use a joint method of wavefield-reconstruction inversion (WRI) combined with FWI.Since WRI has more freedom in the optimization to find a solution,we utilize WRI to inverse the low-frequency part and then perform FWI for the high-frequency part.During the inversion of low-frequency part,we use principal component analysis (PCA) to decrease the data volume by reducing the number of shots.As for the high-frequency part,we use PCA as well as curvelet transformation based FWI to update model.All these approaches ensure the convergence when lack of low-frequency data.Examples based on 2D Marmousi model illustrate that with the help of the proposed method,we can efficiently achieve a FWI robust result even when there is no low-frequency data.
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