Abstract
Microseismic monitoring technique is an important means of ground pressure monitoring to ensure safe, high-efficient and sustainable development of mines. Microseismic data obtained by sensors in mines are easily influenced by non-stationary noises with a wide frequency band, resulting in the lack of available high-quality data for microseismic monitoring. Instead of traditional analysis in frequency domain, this paper introduces a new method, synchrosqueezed wavelet transform (SSWT), which provides a way to decompose data into time domain and frequency domain simultaneously. With higher time-frequency resolution of SSWT spectrum, purer microseismic signals can be extracted from raw data. Besides, two wavelet bases, Morlet wavelet and bump wavelet, are compared to match the microseismic signal in this paper. Two field data with different signal-noise rate (SNR) are used to show the application of the algorithm in the mine industry. The results of data graphical filtering method show that the SSWT has great practical value to extract the microseismic signal from raw data and improves SNR of signals effectively than traditional methods.
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