Speaker: Giusi Ruggiero

Date: Thursday 15th of June 2023, 1:15pm.


Uncertainty quantification in seismic imaging is important for a proper interpretation of the structural elements (e.g., faults and horizons) within the investigated subsurface. Especially in seismic full-waveform inversion (FWI), which is a highly non-linear problem and hence prone to non uniqueness, evaluate uncertainties associated with the estimated subsurface parameters is essential for interpreting inverted models. In this work, we first address uncertainty estimation in elastic FWI by calculating the posterior covariance matrix based on the data-misfit Hessian matrix. In particular, in order to make the computation tractable for large scale problems, we rely on a low rank approximation of the Hessian, which avoids the prohibitive computation of the full matrix. The resulting estimate of uncertainties will be used in a comparison between the homogenized FWI model and a set of homogenized geological models for seeking the best among multiple possible structural interpretations of a given seismic image.