This paper proposes a wavelet denoising algorithm based on an improved lionoptimization threshold strategy. By introducing weight and adjustment factors into the standard lionoptimization algorithm, the local and gl...
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ISBN:
(纸本)9798400709630
This paper proposes a wavelet denoising algorithm based on an improved lionoptimization threshold strategy. By introducing weight and adjustment factors into the standard lionoptimization algorithm, the local and global optimization capabilities are enhanced. During the algorithm verification, forward modeling and theoretical signals generated with different noise were processed, showing that this algorithm significantly improved the signal-to-noise ratio and root mean square error of the signals. When applied to measured transient electromagnetic signals after data preprocessing, the inversion accuracy was further improved. The results demonstrate that this method can accurately separate useful signals from noise and improve signal quality, making it an effective signal denoising method.
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