Enhanced infrasound denoising for debris flow analysis: Integrating empirical mode decomposition with an improved wavelet threshold algorithm

• Published in: Measurement : journal of the International Measurement Confederation Vol. 235; p. 114961
• Main Authors: Dong, Hanchuan, Liu, Shuang, Liu, Dunlong, Tao, Zhigang, Fang, Lide, Pang, Lili, Zhang, Zhonghua
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Debris flow infrasound; Empirical Mode Decomposition; Noise reduction; Sparrow search algorithm; Wavelet threshold; Debris flow infrasound; Empirical Mode Decomposition; Sparrow search algorithm; Wavelet threshold; Noise reduction
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.114961

[Display omitted] •An improved SSA-based wavelet threshold denoising method was proposed by introducing two adjustable factors.•The Hilbert Huang transform was used to decompose the infrasound signal, and the signal was preliminarily filtered based on Pearson correlation coefficient.•Three wavelet threshold parameters were determined in the infrasound simulation.•Experiments on debris flows with different volumes and velocities were conducted in Jiangjia Gully, and the effectiveness of the method was verified. The measurement and analysis of infrasound are widely utilized in debris flow monitoring and early warning. However, the infrasound signals are often contaminated by noise across various frequencies, posing significant challenges for feature extraction. The current methods for infrasound denoising are somewhat rudimentary and face several constraints. This study introduces a novel method for enhancing infrasound signals through denoising. This approach integrates empirical mode decomposition with an enhanced wavelet threshold algorithm, and improves the conventional wavelet threshold function. This study uses the Signal-to-Noise ratio, smoothness and correlation coefficient to fine-tune the wavelet parameters. Experiments in flumes with varying speeds and volumes were conducted to test the effectiveness of the denoising method. The results demonstrate that, compared to traditional methods, the proposed method improves the SNR by averages of 69.56%, 60.91%, and 55.63%. It offers a new alternative for the noise reduction of debris flow infrasound signals.