Full waveform inversion (FWI) infers the subsurface structure information
from seismic waveform data by solving a non-convex optimization problem.
Data-driven FWI has been increasingly studied with various neural network
architectures to improve accuracy and computational efficiency. Nevertheless,
the applicability of pre-trained neural networks is severely restricted by
potential discrepancies between the source function used in the field survey
and the one utilized during training. Here, we develop a Fourier-enhanced deep
operator network (Fourier-DeepONet) for FWI with the generalization of seismic
sources, including the frequencies and locations of sources. Specifically, we
employ the Fourier neural operator as the decoder of DeepONet, and we utilize
source parameters as one input of Fourier-DeepONet, facilitating the resolution
of FWI with variable sources. To test Fourier-DeepONet, we develop two new and
realistic FWI benchmark datasets (FWI-F and FWI-L) with varying source
frequencies and locations. Our experiments demonstrate that compared with
existing data-driven FWI methods, Fourier-DeepONet obtains more accurate
predictions of subsurface structures in a wide range of source parameters.
Moreover, the proposed Fourier-DeepONet exhibits superior robustness when
dealing with noisy inputs or inputs with missing traces, paving the way for
more reliable and accurate subsurface imaging across diverse real conditions