Waveform-preserving source-independent wave-equation-based local-scale traveltime inversion: Application to the SEG Chevron 2014 blind test dataset

Abstract

Full Waveform Inversion (FWI) leverages the amplitude and phase of full wavefield seismic data to obtain high-resolution subsurface structures by minimizing an objective function. However, FWI encounters severe cycle-skipping issues when the seismic data lack low-frequency components or the initial velocity model is significantly different from the true velocity model. Furthermore, an incorrect source wavelet can also directly affect the accuracy of inversion results. To overcome these challenges, we introduce the waveform-preserving source-independent wave-equation-based local-scale traveltime inversion (WPS-LTI) method. This approach aims to recover the low-wavenumber velocity structures as an initial velocity model, while reducing the influence of an incorrect source wavelet. In the WPS-LTI method, seismic data are processed via both convolution and deconvolution with reference traces, resulting in modified observed and synthetic seismic data that share the same seismic wavelet. This approach helps avoid waveform distortions typically introduced by conventional source-independent methods that rely solely on convolution. By preserving the waveform characteristics, we can use cross-correlation to compute more accurate local-scale traveltime differences, thus recovering accurate low-wavenumber velocity structures. Tests conducted on the Marmousi model and the Chevron 2014 blind test dataset demonstrate that the WPS-LTI method can significantly alleviate cycle-skipping issues and reduce the dependence on the source wavelet in FWI.