Full waveform inversion enables us to obtain high-resolution subsurface images. However, estimating the associated uncertainties is not trivial. Hessian-based method gives us an opportunity to assess the uncertainties around a given estimate based on the inverse of the Hessian, evaluated at that estimate. In this work we study various algorithms for extracting information from this inverse Hessian based on a low-rank approximation. In particular, we compare the Lanczos method to the randomized singular value decomposition. We demonstrate that the low-rank approximation may lead to a biased conclusion.

SEG International Exposition and 89th Annual Meeting

Izzatullah, M, van Leeuwen, T, & Peter, D. (2019). Bayesian uncertainty estimation for full waveform inversion: A numerical study. In SEG Technical Program Expanded Abstracts 2019 (pp. 1685–1689). doi:10.1190/segam2019-3216008.1