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In seismic waveform inversion, the reconstruction of the subsurfaceproperties is usually carried out using approximative wave propagation modelsto ensure computational efficiency. The viscoelastic nature of the subsurfaceis often unaccounted for, and two popular approximations--the acoustic andlinearized Born inversion--are widely used. This leads to reconstruction errorssince the approximations ignore realistic (physical) aspects of seismic wavepropagation in the heterogeneous earth. In this study, we show that theBayesian approximation error approach can be used to partially recover fromerrors, addressing elastic and viscous effects in acoustic Born inversion forviscoelastic media. The results of numerical examples indicate that neglectingthe modelling errors induced by the approximations results in very poorrecovery of the subsurface velocity fields.

A Bayesian-based approach to improving acoustic Born waveform inversion of seismic data for viscoelastic media