A fast wavefield reconstruction inversion solution in the frequency domain

Full-waveform inversion (FWI) aims at estimating subsurface physical parameters by minimizing the misfit between simulated data and observations. FWI relies heavily on an accurate initial model and is less robust to measurement noise and physical assumptions in modeling. Compared with FWI, wavefield reconstruction inversion (WRI) is more robust to these uncertainties but faces high computational costs. To overcome these challenges, we have developed a new form of WRI. This reformulation takes the form of a traditional FWI formula, which includes a medium-dependent weight function, and can be easily incorporated into the current FWI workflow. This weight function contains the covariance matrices to characterize the distribution of uncertainties in measurements and physical assumptions. We discuss various options of the theoretical covariance matrix of the new inversion method and find how they relate to various well-known approaches, including FWI, WRI, and extended FWI. On the basis of the preceding comparison, we develop a theoretical covariance matrix definition based on the source. Numerical experiments demonstrate that our method with a source-dependent theoretical covariance matrix is more computationcertain degree of robustness.

Automated summary

Full-waveform inversion (FWI) estimates subsurface parameters by minimizing misfit between simulated and observed data, while wavefield reconstruction inversion (WRI) is more robust but computationally expensive. We developed a new form of WRI by incorporating a medium-dependent weight function into the FWI workflow. This weight function uses covariance matrices to characterize uncertainties in measurements and physical assumptions.