LAUSR

Reflection traveltime information is generally used for producing a kinematically accurate velocity model for seismic imaging. Accurately estimating the traveltime difference between the demigrated and observed data is crucial for data-domain reflection traveltime tomography. To enhance the robustness of signal’s alignment, we propose a new method of segment dynamic image warping (SDIW). It first exploits windowed polynomial fitting for signal processing and then aligns the signals based on point-wise segment-to-segment matching. Compared with the conventional point-to-point matching strategy, our method is more robust and insensitive toward strong random noise. By minimizing the traveltime difference computed by SDIW, data-domain reflection traveltime inversion (DRTI) is then used to find an accurate background velocity model. Synthetic tests show that SDIW provides reliable time shifts between the demigrated and observed data even if the signal-to-noise ratio of input data is rather low. This enables DRTI to automatically reconstruct the deep background velocity model. The subsequent full waveform inversion (FWI) gradually added short-wavelength velocity structures and finally recovers a high-resolution and high-fidelity model. The results demonstrate that the DRTI provides a kinematically accurate background velocity model sufficient for following FWI to fully retrieve the detail of the subsurface structures even when the low frequencies are missing.