A1 Journal article (refereed), original research

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

Open Access publication

Publication Details
Authors: Muhumuza Kenneth, Huttunen Janne, Roininen Lassi, Lähivaara Timo
Publisher: IOP Publishing: Hybrid Open Access
Publication year: 2020
Language: English
Related Journal or Series Information: Inverse Problems
ISSN: 0266-5611
eISSN: 1361-6420
JUFO-Level of this publication: 2
Open Access: Open Access publication
Location of the parallel saved publication: https://arxiv.org/abs/1911.01192


In seismic waveform inversion, the reconstruction of the subsurface properties is usually carried out using approximative wave propagation models to ensure computational efficiency. The viscoelastic nature of the subsurface is often unaccounted for, and two popular approximations--the acoustic and linearized Born inversion--are widely used. This leads to reconstruction errors since the approximations ignore realistic (physical) aspects of seismic wave propagation in the heterogeneous earth. In this study, we show that the Bayesian approximation error approach can be used to partially recover from errors, addressing elastic and viscous effects in acoustic Born inversion for viscoelastic media. The results of numerical examples indicate that neglecting the modeling errors induced by the approximations results in very poor recovery of the subsurface velocity fields.

Last updated on 2020-05-06 at 07:58