Microseismic Source Location Based on Improved Artificial Bee Colony Algorithm: Performance Analysis and Case Study

作     者：Peng Zhang Nuwen Xu Peiwei Xia Tao Zhao Furong Gao Xinchao Ding Biao Li 

作者机构：State Key Laboratory of Hydraulics and Mountain River Engineering College of Water Resource and Hydropower Sichuan University Chengdu China CHN Energy Jinsha River Xulong Hydropower Co. Ltd. China Department of Civil and Environmental Engineering Brunel University London UK PowerChina Northwest Engineering Co. Ltd. Xi'an China School of Geoscience and Technology Southwest Petroleum University Chengdu China 

出 版 物：《Journal of Intelligent Construction》 

年 卷 期：2025年第1卷第3期

页      面：1-15页

基　　金：National Natural Science Foundation of China Natural Science Foundation of Sichuan Province of China 

摘      要：Highly accurate microseismic (MS) localization is the basis for rock damage assessment and disaster warning. The engineering background noise mixed in the MS signal $(s(\varepsilon))$ 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 $(x_{\text{de}}(\varepsilon))$ 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 $(O(h_{0},\ y_{0},\ z_{0}))$ 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.