A least-squares correlation-based full traveltime inversion for shallow subsurface velocity reconstruction

Accurate estimation of shallow subsurface velocity models with complex topography is crucial for statics corrections and imaging deep structures. The correlation-based wave-equation traveltime inversion (CWTI) method is suitable for estimating such shallow subsurface velocity structures. However, the CWTI objective function suffers an inherent resolution-loss problem because the traveltime weighted crosscorrelation misfit does not fall to zero even when the model is perfectly matched. Furthermore, the Born-approximation-based CWTI gradient cannot provide an effective model update during each iteration. To overcome these problems, we have developed a least-squares correlation-based full traveltime inversion (LCFTI) method, in which the least-squares correlation-based objective function was designed to minimize the traveltime weighted difference between the autocorrelation and the crosscorrelation. By incorporating the autocorrelation, LCFTI indicates better convergence and higher resolution than CWTI. The LCFTI model updates are derived using the Rytov approximation to avoid incorrect model updates by emphasizing phase matching. Furthermore, to accurately simulate wave propagation, we use the spectral-element method as the modeling engine, in which the mesh of the complex topography is flexibly represented. Synthetic and field data examples are performed to demonstrate the effectiveness of the proposed method in shallow subsurface velocity reconstruction.