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Seismic imaging is an ill-posed inverse problem that is challenged by noisydata and modeling inaccuracies -- due to errors in the backgroundsquared-slowness model. Uncertainty quantification is essential for determininghow variability in the background models affects seismic imaging. Due to thecosts associated with the forward Born modeling operator as well as the highdimensionality of seismic images, quantification of uncertainty iscomputationally expensive. As such, the main contribution of this work is asurvey-specific Fourier neural operator surrogate to velocity continuation thatmaps seismic images associated with one background model to another virtuallyfor free. While being trained with only 200 background and seismic image pairs,this surrogate is able to accurately predict seismic images associated with newbackground models, thus accelerating seismic imaging uncertaintyquantification. We support our method with a realistic data example in which wequantify seismic imaging uncertainties using a Fourier neural operatorsurrogate, illustrating how variations in background models affect the positionof reflectors in a seismic image.

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification