Mitigating Cycle Skipping in Full-Waveform Inversion Using Partial Matching Filters

Existing methods for addressing cycle skipping in full-waveform inversion (FWI) typically involve either a modification of one of the data sets used to compute the least-squares objective function, or a reformulation of the objective function itself, often in terms of a traveltime (or equivalent) misfit. Both approaches can be successful, but they are reliant to varying extents on the notion of event similarity – that is, the requirement that the observed and modeled data contain the same, distinct, seismic events, even if the corresponding kinematics are different. We introduce a new technique for mitigating cycle skipping in FWI based on partial matching filters. The method accommodates amplitude differences between observed and modeled data, and does not require any major modification to an existing inversion engine. The proposed approach is validated on synthetic and real data sets, including an example where we observe a reduced reliance on event similarity compared to an established cycle skipping mitigation technique.