BackgroundThe volume of aviation gamma spectrum data is immense. If only a central processing unit (CPU) is used for data post-processing, it would be constrained by computational efficiency.PurposeThis study aims to propose a CUDA-based graphics processing unit (GPU) parallel solution that optimally accelerates the denoising of airborne gamma-ray spectral data using wavelet transformation.MethodsFirst, the impact of different block sizes on computational time was tested to determine the optimal block size for processing airborne gamma-ray spectral data. Subsequently, a GPU, instead of a CPU, was used to calculate the acceleration ratio for handling airborne gamma-ray spectral data of different volumes, and wavelet basis functions were used for those with the same data volume. Finally, by introducing white noise to the experimentally measured airborne gamma-ray spectral data, the signal-to-noise ratio of denoised data was calculated to optimize the threshold denoising method suitable for parallel acceleration of the GPU.ResultsThe optimal two-dimensional block sizes for denoising airborne gamma-ray spectral data are 64×64 and 128×128. Among the wavelet basis functions, those that achieved a total time acceleration ratio exceeding 100 compared to CPU processing account for 80%, while those that reached an acceleration ratio exceeding 90 constitute 91%. The coif5 function achieves an acceleration ratio of 353 times whilst the acceleration ratio of the threshold denoising function approaches 570.ConclusionsAll wavelet functions exhibit insufficient denoising effects at low signal-to-noise ratios and excessive denoising effects at high signal-to-noise ratios. Significant denoising can be achieved using hard thresholding of coif5, soft thresholding of coif1, and improved thresholding of bior3.7.
