Abstract: Seismic exploration in complex areas is often constrained by challenging terrains such as mountains, farmlands, and residential areas, making regularized acquisition difficult. Furthermore, in merged-survey processing, discrepancies in acquisition geometries across different areas often hinder seamless data integration at the junction zones. Irregularly sampled seismic data tend to suffer from spatial aliasing and acquisition footprint artifacts during processing. Five-dimensional (5D) regularization is required to sample the data uniformly, thereby enhancing the quality of subsequent modeling, imaging, and interpretation. Existing regularization methods primarily employ the anti-leakage Fourier transform (ALFT) to compute the spectrum of irregularly sampled seismic data, followed by an inverse transform to reconstruct the data onto predefined coordinates. However, for 5D seismic data, the exponential increase in data volume leads to a sharp rise in the computational complexity of non-uniform discrete Fourier transform (NDFT) calculations. Combined with the hundreds of iterations required by ALFT, 5D regularization becomes computationally infeasible for industrial-scale production. Therefore, this study proposes a fast seismic data regularization method using a dual lookup-table strategy. By precomputing Fourier basis function lookup tables, the computational burden of NDFT is significantly reduced; meanwhile, replacing iterative NDFT calculations with table lookup operations requires only a single forward and inverse transform to achieve results comparable to those of conventional ALFT, improving computational efficiency by nearly 100 times. Additionally, by leveraging the property that effective signals converge to the origin along radial lines in the f-k domain, aliasing artifacts are effectively suppressed, significantly improving the regularization accuracy under sparse sampling conditions. Currently, this method has been successfully applied to multiple Sinopec surveys, and the processing results from field data have fully validated its effectiveness and practicality.