Abstract
To reduce noise components from original microseismic waves, a comprehensive fine signal processing approach using the integrated decomposition analysis of the wave duration, frequency spectrum, and wavelet coefficient domain was developed and implemented. Distribution regularities of the wave component and redundant noise on the frequency spectrum and the wavelet coefficient domain were first expounded. The frequency threshold and wavelet coefficient threshold were determined for the identification and extraction of the effective wave component. The frequency components between the reconstructed microseismic wave and the original measuring signal were compared. The noise elimination effect via the scale-changed domain decomposition was evaluated. Interaction between the frequency threshold and the wavelet coefficient threshold in the time domain was discussed. The findings reveal that tri-domain decomposition analysis achieves the precise identification and extraction of the effective microseismic wave component and improves the reliability of waves by eliminating the redundant noise. The frequency threshold and the wavelet coefficient threshold on a specific time window are two critical parameters that determine the degree of precision for the identification of the extracted wave component. This research involves development of the proposed integrated domain decomposition method and provides a diverse view on the fine processing of the microseismic signal.
Highlights
In deep underground mining, microseismic events are ubiquitous
Effective microseismic waves are quite necessary in mining to describe and respond to the potential characteristics in spectral analysis related to time, frequency, and energy, which are prerequisites for further studies on hazard management [11,12,13]
(1) Scale-changed tri-domain decomposition analysis is a comprehensive approach for fine wave processing
Summary
As a direct manifestation of mining-induced tremors, microseismic events are usually considered in the management of major geological dynamic hazards, for example, rock bursts or coal and gas outbursts. For most of the research studies on microseismic activity, determining how to extract valuable information from microseismic events to represent the physical and dynamic characteristics of coal and rock masses is one of the main research interests [1,2,3,4,5,6]. A microseismic wave induced from a microseismic event is acquired by advanced geophysical monitoring equipment. It is a typical complicated nonstationary signal with some unique characteristics, such as short duration, sudden saltation, and rapid attenuation [9, 10]. Effective microseismic waves are quite necessary in mining to describe and respond to the potential characteristics in spectral analysis related to time, frequency, and energy, which are prerequisites for further studies on hazard management [11,12,13]
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