The Optimum Wavelet Base of Wavelet Analysis in Coal Rock Microseismic Signals

• Published in: Advances in Mechanical Engineering Vol. 2014; pp. 552 - 557
• Main Authors: Tang, Shoufeng, Tong, Minming, He, Xinmin
• Format: Journal Article
• Language: English
• Published: London, England Hindawi Limiteds 01.01.2014; SAGE Publications; Sage Publications Ltd. (UK); Sage Publications Ltd; SAGE Publishing
• Subjects: Analysis; Coal; Errors; Hostages; Mean square values; Noise reduction; Optimization; Rock; Roots; Wavelet
• ISSN: 1687-8132; 1687-8140; 1687-8140; 1687-8132
• DOI: 10.1155/2014/537415

Coal rock rupture microseismic signal is characterized by time-varying, nonstationary, unpredictability, and transient property. Wavelet transform is an important method in microseismic signals processing. However, different wavelet bases yield different results when analyzing the same signal. To study the comparability of different wavelet bases in analyzing microseismic signals, the current paper uses the microseismic signals released from coal rock bursting as the research subject. Through the analysis of the properties of commonly used wavelet basis functions and the characteristics of coal rock microseismic signals, the current study found that Coiflet and Symlet wavelets are suitable for analyzing coal rock microseismic signals. Sym 8 and Coif 2 wavelets were found to be suitable for analyzing and denoising coal rock microseismic signals. After Sym 8 wavelet denoising, signal-to-noise ratio (SNR) and the root mean square error were 30.4184 and 1.3109E–07, respectively. After Coif 2 wavelet denoising, the SNR and the root mean square error values were 35.2176 and 1.0312E–07, respectively. The results will aid in the analysis and extraction of coal rock microseismic signals.