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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Physical Sciences and Engineering