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Journal of Intelligent Construction https://doi.org/10.26599/JIC.2023.9180016
Research Article | Open Access | Online first | Published: 13 September 2023

Microseismic source location based on improved artificial bee colony algorithm: Performance analysis and case study

Show Author's Information Peng Zhang1,  Nuwen Xu1( ),  Peiwei Xiao1,2,  Tao Zhao3,  Furong Gao2,  Xinchao Ding4,  Biao Li5( )
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
CHN Energy Jinsha River Xulong Hydropower Co., Ltd., Tibetan Autonomous Prefecture of Garzê 627950, China
Department of Civil and Environmental Engineering, Brunel University, London UB8 3PH, UK
PowerChina Northwest Engineering Co., Ltd., Xi’an 710065, China
School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
Keywords:
underground engineering, microseismic (MS) location, signal denoising arrival time picking, artificial bee colony (ABC) algorithm
Cite this article:
Zhang P, Xu N, Xiao P, et al. Microseismic source location based on improved artificial bee colony algorithm: Performance analysis and case study. Journal of Intelligent Construction, 2023, https://doi.org/10.26599/JIC.2023.9180016
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Abstract Full text About this article

Highly accurate microseismic (MS) localization is the basis for rock damage assessment and disaster warning. The engineering background noise mixed in the MS signal (s(ε)) seriously affects the subsequent analysis of the MS signal. A noise reduction method of singular spectral analysis–complementary ensemble empirical mode decomposition–wavelet threshold (SSA–CEEMD–WT) is proposed. The CEEMD, CEEMD–WT, and proposed methods are used for denoising the noisy Ricker wavelet. The signal-to-noise ratio (SNR) of the denoised signal (xde(ε)) by the proposed method is 56.77% and 37.88% higher than those of CEEMD and CEEMD–WT methods, respectively. Moreover, an adaptive artificial bee colony (ABC) algorithm is applied for MS source (O(h0, y0, z0)) location. The time to quantile difference is introduced as the objective function. The blast positioning test results prove that the proposed method improves the positioning accuracy of particle swarm optimization (PSO) algorithm and simulated annealing PSO (SA-PSO) algorithm by 44.12% and 47.64%, respectively. The MS positions of underground caverns reveal that the calculated clusters of MS events using the adaptive ABC algorithm are more concentrated at the structural plane and appearance deformation failure location and in good agreement with field survey and routine monitoring data.


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About this article

Microseismic source location based on improved artificial bee colony algorithm: Performance analysis and case study

Show Author's information Peng Zhang1, Nuwen Xu1( ), Peiwei Xiao1,2, Tao Zhao3, Furong Gao2, Xinchao Ding4, Biao Li5( )
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
CHN Energy Jinsha River Xulong Hydropower Co., Ltd., Tibetan Autonomous Prefecture of Garzê 627950, China
Department of Civil and Environmental Engineering, Brunel University, London UB8 3PH, UK
PowerChina Northwest Engineering Co., Ltd., Xi’an 710065, China
School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China

Abstract

Highly accurate microseismic (MS) localization is the basis for rock damage assessment and disaster warning. The engineering background noise mixed in the MS signal (s(ε)) seriously affects the subsequent analysis of the MS signal. A noise reduction method of singular spectral analysis–complementary ensemble empirical mode decomposition–wavelet threshold (SSA–CEEMD–WT) is proposed. The CEEMD, CEEMD–WT, and proposed methods are used for denoising the noisy Ricker wavelet. The signal-to-noise ratio (SNR) of the denoised signal (xde(ε)) by the proposed method is 56.77% and 37.88% higher than those of CEEMD and CEEMD–WT methods, respectively. Moreover, an adaptive artificial bee colony (ABC) algorithm is applied for MS source (O(h0, y0, z0)) location. The time to quantile difference is introduced as the objective function. The blast positioning test results prove that the proposed method improves the positioning accuracy of particle swarm optimization (PSO) algorithm and simulated annealing PSO (SA-PSO) algorithm by 44.12% and 47.64%, respectively. The MS positions of underground caverns reveal that the calculated clusters of MS events using the adaptive ABC algorithm are more concentrated at the structural plane and appearance deformation failure location and in good agreement with field survey and routine monitoring data.

Keywords: underground engineering, microseismic (MS) location, signal denoising arrival time picking, artificial bee colony (ABC) algorithm

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Publication history

Received: 28 June 2023
Revised: 30 July 2023
Accepted: 01 August 2023
Published: 13 September 2023

Copyright

© The Author(s) 2023. Published by Tsinghua University Press.

Acknowledgements

The authors are grateful for the financial support from the National Natural Science Foundation of China (Nos. 42277461 and 42177143), Natural Science Foundation of Sichuan Province of China (No. 2022NSFSC0005), and Sichuan Science and Technology Program (No. 2023NSFSC0812).

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