oa Accelerating 2-D Full Wavefield Forward Modeling via Frequency Interpolation with a Tiny Attention U-Net Based Model

Joint migration inversion, an innovative geophysical method, smartly merges velocity model building with seismic imaging using full wavefield migration algorithms. It uses the full seismic reflection response - including multiple scattering - in a unified framework to produce high resolution geological structure images. Despite precision of FWM, it confronts heavy computational demands, memory needs, and reliance on strong computing resources. This paper proposes a deep learning-based seismic interpolation acceleration strategy centered on a tiny Attention U-Net based model. Designed for efficient frequency domain sparse wavefield interpolation and reconstruction, it reduces full wavefield forward modeling costs. Trained on a 2-D lens-shaped velocity model, the model adaptively learns complex mappings between sparse and complete wavefields, reproducing 2-D numerical simulations while cutting computation time. With up to 50 % missing seismic data, this approach improves efficiency by about 40 % vs. conventional FWM. Integrating trained model into JMI further saves about 30 % compute time under simplified conditions, affirming its advantages and potential in frequency domain forward modeling.