Full waveform inversion with an amplitude increment coding-based data selection

Abstract

Full waveform inversion (FWI) is an efficient tool to build the subsurface velocity models. However conventional FWI suffers from the cycle skipping problem, which causes FWI to fail in converging to the global minimum. A good initial model can mitigate this problem, but it is hard to be provided. Low frequencies in the observed data are helpful to recover the low-wavenumber components of the subsurface velocity models, which can provide good initial models. However, field data usually lack low frequencies because of the physical limitation of the instruments or the environmental noise. In addition, multiscale approach may not work well to tackle the cycle skipping problem when there isn’t sufficient low-frequency information in the observed data. Therefore, we proposed an amplitude increment coding-based data selection method to find which parts of the data are mismatched, and set these parts of data to 0 to mitigate the cycle skipping problem. In this case, we use the global-correlation misfit function which behaves better in mitigating the interference of the incorrect amplitude information and highlighting the phase information with weaker nonlinearity. In addition, the amplitude increment coding-based data selection method can be combined with the encoded blended-source scheme to improve computational efficiency. Numerical tests on Marmousi model demonstrate that FWI with an amplitude increment-based data selection method can generate convergent results when the observed data lack low frequencies.