Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
This work addresses the high computational costs of uncertainty quantification for seismic imaging practitioners, though it is incremental as it builds on existing neural operator methods.
The authors tackled the computational expense of uncertainty quantification in seismic imaging by developing a Fourier neural operator surrogate that maps seismic images between background models, enabling accelerated uncertainty quantification with only 200 training pairs.
Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies -- due to errors in the background squared-slowness model. Uncertainty quantification is essential for determining how variability in the background models affects seismic imaging. Due to the costs associated with the forward Born modeling operator as well as the high dimensionality of seismic images, quantification of uncertainty is computationally expensive. As such, the main contribution of this work is a survey-specific Fourier neural operator surrogate to velocity continuation that maps seismic images associated with one background model to another virtually for free. While being trained with only 200 background and seismic image pairs, this surrogate is able to accurately predict seismic images associated with new background models, thus accelerating seismic imaging uncertainty quantification. We support our method with a realistic data example in which we quantify seismic imaging uncertainties using a Fourier neural operator surrogate, illustrating how variations in background models affect the position of reflectors in a seismic image.