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Abstract Full Waveform Inversion (FWI) is nowadays commonly used for high-resolution velocity model building. Conventional FWI with least-squares objective function often suffers from cycle-skipping problem. This issue becomes prominent in subsurface structures with high complexity, significant deviations between the initial and true models, or a lack of low-frequency content in the data. To address the cycle-skipping problem, we present a novel Joint-Domain Full Waveform Inversion (JDFWI) framework optimizing travel time accuracy in both data and model domains. The model domain uses delay time common image gathers (DTCIGs) for travel time measurement. In the data domain, travel time misfit is extracted without amplitude information. Thus, integrating a time-lag-based misfit function efficiently overcomes the cycle-skipping problem. We first validated our JDFWI with a controlled synthetic test and then applied our proposed JDFWI workflow to a challenging ultra-shallow water ocean-bottom node (OBN) dataset from the South Caspian Basin. While conventional FWI suffers from cycle-skipping problem, JDFWI delivers high-resolution and accurate velocity models.
Yang et al. (Tue,) studied this question.