TY - JOUR
T1 - A Synthetic Solution for Identification and Extraction of the Effective Microseismic Wave Component Using Decomposition on Time, Frequency, and Wavelet Coefficient Domains
AU - Zhang, Mingwei
AU - Meng, Qingbin
AU - Liu, Shengdong
AU - Shimada, Hideki
N1 - Funding Information:
The financial and general support for this research provided by the Fundamental Research Funds for the Central Universities (no. 2015QNA62) and the National Key R&D Program of China (no. 2016YFC0600900) is gratefully acknowledged. This fund covers the costs of publishing the work. In addition, the authors would like to express their sincere gratitude to the Xingcun Coal Mine for the support with the field experiments.
Publisher Copyright:
© 2017 Mingwei Zhang et al.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85038919395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85038919395&partnerID=8YFLogxK
U2 - 10.1155/2017/3875170
DO - 10.1155/2017/3875170
M3 - Article
AN - SCOPUS:85038919395
VL - 2017
JO - Shock and Vibration
JF - Shock and Vibration
SN - 1070-9622
M1 - 3875170
ER -