Adaptive Waveform Inversion - FWI Without Cycle Skipping - Applications

Conventional FWI suffers from cycle skipping if the starting model is inadequate at the lowest frequencies present in a dataset. The newly developed technique of adaptive FWI overcomes cycle skipping, and is able to invert normal bandwidth data beginning from an inaccurate velocity model. Here we apply the method to data extracted from a 3D field model, and show that the new method outperforms conventional FWI when starting at higher frequencies than have previously been used to invert this field dataset. We also apply the new methodology to a synthetic dataset that is not cycle skipped, but that is dominated by reflected rather than refracted arrivals. In this case, we show that adaptive FWI also produces a superior result because it has enhanced sensitivity to reflection data, and is able to update the velocity macro-model successfully using reflection-only data.