1887

Abstract

Summary

The conventional full waveform inversion (FWI) often uses local optimization algorithm to update velocity model. So the initial model we provide should be good enough to avoid local minimum. Abundant low-frequency information can compensate for the inaccuracy of the initial model and help to avoid cycle-skipping. In this paper, we proposed a wavelet filter based low-frequency data reconstruction method. We extracted the subsurface impulse responses using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), and convolved the broad-band impulse responses with band-limited low-frequency source wavelets to obtain low-frequency data. The convolution process is equivalent to filtering using low-frequency source wavelets. The accuracy analysis demonstrated that the reconstructed data can meet the requirement of FWI. We proposed a new strategy for multiscale time domain full waveform inversion (TDFWI), which using a series of low-frequency reconstructed data as observed data. One distinct advantage is that the wavelet is accurately known for the reconstructed data, which reduces the uncertainty of FWI. This strategy avoids the effect of source wavelets uncertainty on inversion results, and avoids the simultaneously pre-process of data and wavelets. Numerical example shows that our strategy can avoid cycle-skipping effectively and can converge on a bad initial model.

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/content/papers/10.3997/2214-4609.201600643
2016-05-30
2024-03-29
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References

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