A Ground-Penetrating Radar Clutter Suppression Algorithm Integrating Signal Processing and Image Fusion

This study presents a clutter suppression algorithm for underground mine ground-penetrating radar (GPR) B-scan profiles, integrating the sparrow search algorithm (SSA), variational mode decomposition (VMD), and image fusion via the improved nonsubsampled shearlet transform (INSST), aimed at addressing clutter under strict explosion-proof conditions. Initially, the raw B-scan data undergoes preprocessing to derive a grayscale profile. Using SSA, an envelope entropy fitness function is introduced to automatically optimize VMD with respect to the average trajectory of the grayscale profile. Finally, enhanced non-subsampled shearlet transform (NSST) processing is applied to the synthesized profile using a scale-direction adaptive threshold with an L2 norm square constraint and a flexible threshold function. This approach reduces Gibbs artifacts and oversmoothing while retaining valid signals within the threshold range. In simulated profiles, the proposed algorithm demonstrates strong robustness across different noise standard deviations, significantly improving image quality metrics such as peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM), while reducing the root-mean-square error (RMSE). Notably, in measured mining profiles, the proposed algorithm outperforms eight other methods by better preserving edge details. Compared to the suboptimal algorithm, the target-to-noise ratio (TNR) improves by 36% in mining fault profile I and by 17% in coal seam floor profile II. Finally, a sensitivity analysis of the proposed algorithm's parameters was conducted.