GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 3.3 ______ ___ GNGTS 2023 Fig.3 On the left part, comparison between the observed data and the predicted data from the estimated mean model; on the right, the initial data calculated from the started model from which the inversion procedure was initiated and the evolution of the negative log-likelihood values during the GB-MCMC sampling. The vertical red dotted line indicates the end of the burn-in. REFERENCES Aleardi M.; 2021: A gradient-based Markov chain Monte Carlo algorithm for elastic pre-stack inversion with data and model space reduction . Geophysical Prospecting 69(3), doi:10.1111/1365-2478.13081 Gebraad L.; Boehm C. & Fichtner A.; 2020: Bayesian elastic full-waveform inversion using Hamiltonian Monte Carlo. Journal of Geophysical Research, Solid Earth, 125(3), e2019JB018428. Hastings W. K.; 1970: Monte Carlo sampling methods using Markov chains and their applications . Biometrika, 57, 97–109. Louboutin M., Lange M., Luporini F., Kukreja N., Witte, P.A., Herrmann F.J., Velesko P., Gorman G.J.; 2019: Devito (v3.1.0): an embedded domain specific language for finite differences and geophysical exploration. Geoscientific Model Development 12, 1165–1187. Mosegaard K. and Tarantola A.; 2002: Probabilistic approach to inverse problems . International Geophysics Series, 81, 237–268. Virieux J. and Operto S.; 2009: An overview of full-waveform inversion in exploration geophysics . Geophysics, 74, no. 6, WCC1–WCC26, doi:10.1190/1.3238367. Zhao Z. and Sen M. K.; 2021: A gradient-based Markov chain Monte Carlo method for full-waveform inversion and uncertainty analysis . Geophysics, 86, no. 1, P. R15-R30. Sean Berti, sean.berti@unifi.com