Micro-seismic monitoring in mines based on cross wavelet transform

© 2016 Techno-Press, Ltd.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 subjecte...

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Main Authors: Huang, L., Hao, H., Li, X., Li, Jun
Format: Journal Article
Published: 2016
Online Access:http://hdl.handle.net/20.500.11937/50075
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author Huang, L.
Hao, H.
Li, X.
Li, Jun
author_facet Huang, L.
Hao, H.
Li, X.
Li, Jun
author_sort Huang, L.
building Curtin Institutional Repository
collection Online Access
description © 2016 Techno-Press, Ltd.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.
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spelling curtin-20.500.11937-500752017-09-13T15:34:02Z Micro-seismic monitoring in mines based on cross wavelet transform Huang, L. Hao, H. Li, X. Li, Jun © 2016 Techno-Press, Ltd.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. 2016 Journal Article http://hdl.handle.net/20.500.11937/50075 10.12989/eas.2016.11.6.1143 restricted
spellingShingle Huang, L.
Hao, H.
Li, X.
Li, Jun
Micro-seismic monitoring in mines based on cross wavelet transform
title Micro-seismic monitoring in mines based on cross wavelet transform
title_full Micro-seismic monitoring in mines based on cross wavelet transform
title_fullStr Micro-seismic monitoring in mines based on cross wavelet transform
title_full_unstemmed Micro-seismic monitoring in mines based on cross wavelet transform
title_short Micro-seismic monitoring in mines based on cross wavelet transform
title_sort micro-seismic monitoring in mines based on cross wavelet transform
url http://hdl.handle.net/20.500.11937/50075