Full waveform inversion with low frequency reconstructed data

Full Waveform Inversion (FWI) requires the minimization of a highly non-linear objective function which makes the inversion suffer from cycle-skipping. To overcome this issue, we need to have an accurate initial velocity model and/or input seismic data with good low-frequency content. However, low frequencies can be very noisy, and conventional noise attenuation tools fail to recover the useful signal. In this abstract, we propose to apply a novel low-frequency reconstruction method to condition the data for Full Waveform Inversion. The reconstruction is done from the higher frequencies using a recursive filter which is estimated from the data itself. We apply the low-frequency reconstruction method on synthetic data to show its high accuracy even in presence of strong noise extracted from field data. We successfully performed FWI using low frequency reconstructed field data starting from simple velocity models. The reconstructed low frequencies help to mitigate the cycle-skipping observed when these frequencies cannot be utilized in the inversion and the initial models are not accurate. Results demonstrate the effectiveness of the novel data reconstruction method and show its benefits in reducing the turnaround time for building accurate velocity models by FWI, when starting from less suitable initial velocity models.