Estimation of an acoustic velocity model for the crop M12a Seismic line using a gradient-based full waveform inversion

his work deals with the application of a Full Waveform Inversion (FWI) procedure to increase the resolution of an acoustic ve-locity model related to a part of the M12A CROP marine seismic profile. The CROP M12A seismic line was acquired during the Italian Deep Crust Project (CROP), aimed at investigating the structure of the deep crust in Italy. In (Tognarelli et al., 2010) the re-corded data were re-processed to enhance the visibility and the resolution of the structures at the shallow depth up to 3-4 s two-way travel time. Nowadays, FWI represents an important tool to build a high-resolution velocity model of the subsurface from active seismic data. Such model is obtained as the global minimum of some misfit function, designed to measure the difference between the observed and the modelled data. In general, the misfit function is highly non-linear with the presence of many local minima due to the well-known cycle skipping effect. Therefore, the optimization problem is solved by means of an iterative gradient-based method, starting from a model as close as possible to the global minimum of the objective func-tion. Besides, the application of FWI to real data requires dedicated specific operations aimed at improving the S/N ratio and obtaining observed data that can be reliably reproduced by a modelling algorithm. In this work, we present an application of acoustic FWI on a part of CROP M12A seis-mic profile. Specific processing operations are applied on both predicted and observed data to increase the robustness of the inversion procedure, thus improving the reliability of the final model estimation. The predicted data are obtained by solving the 2D acoustic wave equation, whereas in the local optimization procedure the steepest descent algorithm is employed. The misfit function used is based on the L2 norm difference between the predicted and observed envelopes of the seismograms. As the starting model, we used the model obtained by (Tognarelli et al., 2010) through the Migration Velocity Analysis (MVA) and used for the post-stack depth migration of the data. To validate the FWI final model, we check the improvements on the flattening of the events in the common-image-gathers (CIGs) after pre-stack depth migration.