Abstract

Summary Full Waveform Inversion (FWI) has the potential to provide high-resolution subsurface models. However, the objective function of FWI is strongly nonlinear, and it can easily fall into a local minimum due to circle skipping. In order to avoid circle skipping, multiscale waveform inversion is proposed. Nevertheless, with the interference of noise, the FWI has difficulty obtaining accurate results for real data. FWI generally requires low-frequency data with high signal-to-noise ratio (SNR) and calculates a reliable model with the data, which is employed as the initial model in high-frequency inversion. To overcome this limitation on data quality, we propose a full-model staining algorithm in the frequency domain and take the advantage of wavefield superposition to optimize the gradient of FWI. Numerical examples indicate that the combination of the full-model staining algorithm and the limited memory quasi-Newton FWI method can successfully handle the noisy data to calculate accurate results. The proposed full-model staining algorithm is promising to deal with real data of low SNR for FWI.

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