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Earthquakes and Structures
  Volume 11, Number 6, December 2016 , pages 1143-1164
DOI: https://doi.org/10.12989/eas.2016.11.6.1143
 


Micro-seismic monitoring in mines based on cross wavelet transform
Linqi Huang, Hong Hao, Xibing Li and Jun Li

 
Abstract
    Time Delay of Arrival (TDOA) estimation methods based on correlation function analysis play an important role in the micro-seismic event monitoring. It makes full use of the similarity in the recorded signals that are from the same source. However, those methods are subjected to the noise effect, particularly when the global similarity of the signals is low. This paper proposes a new approach for micro-seismic monitoring based on cross wavelet transform. The cross wavelet transform is utilized to analyse the measured signals under micro-seismic events, and the cross wavelet power spectrum is used to measure the similarity of two signals in a multi-scale dimension and subsequently identify TDOA. The offset time instant associated with the maximum cross wavelet transform spectrum power is identified as TDOA, and then the location of micro-seismic event can be identified. Individual and statistical identification tests are performed with measurement data from an in-field mine. Experimental studies demonstrate that the proposed approach significantly improves the robustness and accuracy of micro-seismic source locating in mines compared to several existing methods, such as the cross-correlation, multi-correlation, STA/LTA and Kurtosis methods.
 
Key Words
    micro-seismic monitoring; source location; TDOA; cross wavelet transform
 
Address
Linqi Huang, Xibing Li: School of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, China

Hong Hao, Jun Li: Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Bentley, WA6102, Australia
 

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