Seismic data reconstruction via non-uniform Fourier transform and implicit neural network denoising prior

Abstract Irregular sampling of seismic data on non-equispaced spatial grids presents significant challenges in seismic data acquisition and imaging, resulting in reduced quality and continuity of subsurface structures. Seismic data reconstruction plays a pivotal role in transforming irregularly sampled data into equispaced grids for improved imaging quality. Conventional methods combined the curvelet transform with the non-uniform discrete Fourier transform (NDFT) to reconstruct seismic data but often suffered from spectral leakage and incomplete continuity of seismic events, limiting their practical effectiveness. To address this, we propose an advanced seismic data reconstruction approach that integrates the NDFT with an implicit denoising neural network prior. The proposed method leverages the NDFT to map irregular sampling points accurately to equispaced grids while incorporating the denoising neural network as a prior to suppress noise and enhance the continuity of seismic events. Numerical experiments on synthetic and field datasets demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method achieves better reconstruction accuracy, and preserves continuity more effectively compared to traditional curvelet-based method.