Efficient 2-D Seismic Imaging via AI-Driven Frequency-Domain Interpolation and HPC Kernel Acceleration
J. Zhao (The Cyprus Institute)
N. Akram (Universidade de Lisboa)
N. Savva (University of Cyprus)
E. Verschuur (TU Delft - Civil Engineering & Geosciences)
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Abstract
This research presents a hybrid AI–HPC acceleration framework designed to overcome the computational barriers of 2-D seismic imaging based on Full Wavefield Modeling. By exploiting the decoupling of frequency components, the proposed method reduces the computational burden of physics-based simulations through a sparse frequency sampling strategy. The omitted frequency slices are accurately reconstructed using an Attention U-Net, which employs attention mechanisms to filter noise and preserve structural details during the interpolation process. To further optimize runtime efficiency, the performance-hotspot wavefield propagation and scattering kernels are optimized via Numba-based Just-In-Time compilation. This implementation strategy eliminates the overhead of the Python interpreter, enabling native machine-code execution with SIMD vectorization and multi-core parallelism. Numerical experiments conducted on synthetic models with varying geological complexity confirm that this framework delivers substantial reductions in computation time while maintaining high wavefield accuracy, stable inversion convergence, and similar resolution in reflectivity imaging.