GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 3.3 ______ ___ GNGTS 2023 A Bayesian Approach to Elastic Full-Waveform Inversion: Application to a Synthetic Dataset S. Berti 1,2 , M. Aleardi 1 , E. Stucchi 1 1 Earth Sciences Department, University of Pisa, Italy 2 Earth Sciences Department, University of Florence, Italy INTRODUCTION Seismic full-waveform inversion (FWI) is being increasingly applied at both exploration and global scales for the determination of high-resolution subsurface models (Tarantola 1984; Virieux and Operto, 2009). In FWI, seismic waveforms are exploited to update subsurface model parameters by trying to match the recorded data with the estimated data. There are some well-known issues often related to the traditional FWI problem. Since the seismic data-model relationship is strongly nonlinear, FWI model updates are often trapped in local minima of the objective function (that usually measures the misfit between observed and estimated data), which are caused by the lack of low frequencies in the data and by inaccurate starting models. Also, focusing on the best-fitting model only (i.e., deterministic inversion) impedes a complete assessment of the uncertainty affecting the recovered solution (i.e., probabilistic framework). Multiparameter elastic FWI is concerned with the simultaneous determination of two or more subsurface elastic properties (i.e., P-wave velocity, S-wave velocity and density) and this adds a further set of serious challenges to practical FWI application. In addition, elastic FWI generally requires extremely long computing time to achieve convergence (also the elastic forward modelling is more computationally expensive than the acoustic one). We seek the solution for elastic FWI in a Bayesian inference framework to address these issues (Mosegaard and Tarantola, 2002; Gebraad et al, 2020). Bayesian inference provides a systematic framework for incorporating and propagating the uncertainties in observed data, prior knowledge and forward operator into the uncertainties affecting the recovered model. The final solution of a Bayesian inversion is the so-called posterior probability density (PPD) function in the model space which fully quantifies the uncertainties in the recovered solution.