AI-driven seismic wavefield reconstruction via frequency interpolation for efficient Joint Migration Inversion
Naveed Akram (The Cyprus Institute)
J. Zhao (The Cyprus Institute)
D.J. Verschuur (TU Delft - Applied Geophysics and Petrophysics)
Nikos Savva (University of Cyprus)
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Abstract
With a focus on geo-imaging applications for the energy transition, we are looking for affordable, but still accurate seismic imaging methodologies. One of those recently developed methods is Joint Migration Inversion, which involves the joint estimation of the seismic reflectivity image and the background propagation velocity model. This method operates in the frequency domain and is based on recursive wavefield propagation, while including all scattering and transmission effects. The involved full wavefield modeling engine is the most time-consuming part of the JMI process, so accelerating this has direct impact on the overall costs. One option is making use of the fact that the modeling can be done independently per frequency component, such that we can model the data for a subset of these frequencies and use interpolation to obtain the data at missing frequencies. This papers studies the use of a neural network (NN) approach for this interpolation process. We investigate the accuracy of the interpolation process under different sub-sampling ratios and using regular or irregular subsampling. The counter-intuitive result is that regular subsampling gives slightly better results. Moreover, we demonstrate that we can go down to 66% missing frequencies with the currently used NN based on the cGAN approach.