A novel approach for seismic signal denoising using optimized discrete wavelet transform via honey badger optimization algorithm

Seismograms are a vital source of information in seismic signal processing. These records are contaminated by noise, which should be reduced before processing in seismic applications. Wavelet-based methods have shown great success in denoising seismic signals, and their performance is determined based on the wavelet transform (WT) parameters. Therefore, in this paper, four swarm optimization algorithms are suggested to estimate the WT parameters optimally to mitigate the noise in the seismic signal. First, this article examines the efficacy of the optimization algorithms for achieving the minimum fitness value and better convergence curve. Then, based on the fitness value, optimal wavelet transform parameters are obtained to perform discrete wavelet transform on the seismic signal. The analysis is conducted on seismic trace and seismic section to disclose the dominance of the proposed algorithm over the traditional approaches. The results demonstrate the effectiveness of our proposed strategy for seismic signal denoising over other traditional approaches.