Discrete wavelet transform denoising method efficiency evaluation for processing pulse signals with harmonic components
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This article reviews the problem of parameter selection for denoising methods based on the Discrete Wavelet Transform (DWT) for processing geo-signals with various noise types and external interference, followed by evaluating the effectiveness in detecting recurring signal patterns. The study reviews the theoretical impact of denoising parameters, existing wavelet and decomposition level selection methods, publications on DWT applications in different fields, and the computational challenges of increasing decomposition levels for microcontrollers. Experimental results of DWT denoising application on field-gathered signals recorded in different environments, presented as average SNR changes for specific DWT parameter combinations. Comparison of results by decomposition levels showed gradual improvements in efficiency with certain wavelets and significant drops after specific levels in some cases due to the filtering of typical samples, which emphasizes the need to review DWT parameters only in the scope of specific parameter combinations. Notable anomalies in efficiency due to the non-stationary nature of signals and parameter resonance with noise or patterns were also observed, requiring further research. Based on the findings, the most effective parameter combinations for denoising the studied geo-signal were identified, with a particularly optimal combination of three decomposition levels, hard thresholding, and rbio3.3 wavelet, which preserved and even amplified signal energy while enabling the detection of typical fragments at distances of 120–100 meters
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