We present an experience of acoustic FWI on a noisy 2D land dataset to the end of providing a velocity model for direct geological interpretation. We first perform a dedicated processing trying to improve the data quality and we select as the input for the inversion direct, refracted and diving waves. In fact, notwithstanding the processing efforts, reflections are nearly absent. Next, we perform a sequence of FWIs. Since we wish to use the estimated velocity for the interpretation of the area, it is necessary that the estimation is not biased by unverified a-priori geological hypotheses. Therefore, to derive a low-resolution velocity model, we apply two runs of genetic-algorithm FWI (GA-FWI) with different data misfit functions based on envelopes and on waveforms. In fact, GA do not start from a given velocity model, thus risking to bias the final outcome, but from an ensemble of models randomly selected within large search ranges. The GA velocities are then used as starting model for a gradient-based FWI which yields an improved model, appropriate for evaluating different geological hypotheses. In two locations, the check-shot velocities of exploratory wells show a good matching with the 1D velocity profiles extracted from the final model.


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