Open AccessApplication of Synchrosqueezed Wavelet Transform in Microseismic Monitoring of Mines
Author(s) -
Yanling Shi,
Da Zhang,
Junlin Hu,
Ruwei Dai
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/384/1/012075
Subject(s) - microseism , morlet wavelet , wavelet , computer science , wavelet transform , signal (programming language) , frequency domain , data mining , acoustics , artificial intelligence , geology , seismology , discrete wavelet transform , computer vision , programming language , physics
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.