![]() ![]() We aim to augment it further to obtain an approximate inverse Hessian for uncertainty quantification in FWI. For seismic inverse problems, the limited-memory BFGS (L-BFGS) method prevails as the most efficient quasi-Newton method. ![]() In this study, we amend the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm to perform uncertainty quantification for large-scale applications. Even for common geophysical inverse problems its calculation is beyond the computational and storage capacities of the largest high-performance computing systems. In theory, uncertainty quantification is related to the inverse Hessian (or the posterior covariance matrix). However, proper uncertainty quantification is often lacking in current applications. SUMMARY Full-waveform inversion has become an essential technique for mapping geophysical subsurface structures. ![]()
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