Experience of acoustic FWI on seismic land data

After more then ten years of research and development, Full Waveform Inversion (FWI) still remains challenging and even now there are many topics that are open to debate. The solution of the inverse problem, the limitation of the computational costs and the estimation of a good initial models from where to start the inversion, are just some of these topics. On the other hand, FWI applicability and success is also very dependent on the characteristics of the input seismic data and in particular: the signal-to-noise ratio, the maximum recorded offset and the low frequency content. For this reasons, actual and successful examples found in the literature mainly refer to marine seismic applications (Sirgue et al., 2010, Guasch et al., 2015), while limited experiences refer to land data FWI (Brossier et al., 2009, Plessix et al., 2010, Al-Yaqoobi et al., 2013, Galuzzi et al., 2016). This is mainly due to the generally poor quality of the gathers recorded onshore, but also to the difficulty on the choice and estimation of the source wavelet from the actual data and finally to the topography and near surface effects that alter and contaminate with noise the gathers. Indeed, if the kinematic of the events is the main information that we want to invert, as it is discussed here, processing can be useful to partially circumvent this limitations and to recover the coherency of the events without taking into account the amplitude and phase behaviours. In this work, we present an experience of acoustic genetic algorithm (GA) driven FWI on a 2D seismic line acquired onshore, in the South Tuscany, aimed at estimating a low-frequency low-wavenumber P-wave velocity model, that could be used as starting model for a subsequent gradient based FWI. In the first part of this work, we discuss the processing steps applied to improve the signal-to-noise ratio of the gathers and finally to generate the observed data. In the second part, we describe the stochastic FWI employed that makes use of a two-grid approach, a coarse grid for the inversion and a fine grid for the modeling and the GA as the inversion engine. This methodology is discussed in Sajeva et al. (2014) and in Tognarelli et al. (2015) where is applied on synthetic and field marine data.