Two-grid full-waveform Rayleigh-wave inversion via a genetic algorithm — Part 2: Application to two actual data sets

We present the application of our two-grid genetic-algorithm Rayleigh-wave full-waveform inversion (FWI), which has been described in a companion paper, to two actual data sets acquired in Luni (Italy) and Grenoble (France), respectively. As our technique employs 2D elastic finite difference modeling for solving the forward problem, the observed data were 3D to 2D corrected prior to the inversion. To limit the computing time, both the inversions were focused on predicting low-resolution, smooth models by using quite coarse inversion grids. The wavelets for the FWI were estimated directly from the observed data via the Wiener method. In the Luni case, due to the strong dispersion effects on the data, to strengthen the inversion both envelopes and waveforms were considered in the objective function and an offset-marching strategy was applied. Though no a priori information was exploited, both the outcomes of the Luni and Grenoble data inversion were fair. The predicted Luni Vs model shows a strong velocity increase from about 3 to 6 m and velocity inversions have been detected at around 2 m and 9 m depth. Analyzing the dispersion spectra, it results that the predicted Luni data reasonably reproduced the waveforms related to the fundamental mode and, likely, a small part of those related to the 1st higher mode. Concerning the Grenoble example, the predicted Vs model coincides reasonably well with the long-wavelength structures presented in the Vs profiles obtained from nearby boreholes. The data reconstruction is generally satisfactory and when mismatches occur between the predicted and observed traces, the phase differences are always within half-periods. The fair inversion outcomes suggest that both the predicted Luni and Grenoble model would likely be adequate initial models for local FWI, which could further increase the resolution and the details of the estimated Vs models