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| Adaptive first-arrival picking method for microseismic signals in mines by integrating energy dissipation features and multi-scale complexity |
| XU Huicong1, 2, LI Kai1, LAI Xingping1, 2*, SHAN Pengfei1, 2, YANG Shangtong3, 4, XI Xun5, YAN Zhongming6 |
(1. College of Energy and Mining Engineering, Xi?an University of Science and Technology, Xi'an, Shaanxi 710054, China;
2. Key Laboratory of Western Mines and Hazard Prevention of China Ministry of Education, Xi'an University of Science and
Technology, Xi'an, Shaanxi 710054, China; 3. State Key Laboratory of Intelligent Construction and Healthy Operation
and Maintenance of Deep Underground Engineering, China University of Mining and Technology, Xuzhou, Jiangsu
221116, China; 4. Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow,
G11XJ, UK; 5. School of Resources and Safety Engineering, University of Science and Technology Beijing,
Beijing 100083, China; 6. Shaanxi Binchang Mining Group Co., Ltd., Xianyang, Shaanxi 712000, China) |
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Abstract Microseismicity, as an “acoustic manifestation” of nonlinear mechanical behavior in rock mechanics and engineering, plays a crucial role in the accurate picking of first-arrival times for the prediction and early warning of deep coal-rock dynamic disasters in China. Due to the combined effects of complex operational environments and unique geological conditions, microseismic signals in deep mining areas typically exhibit high-dimensional, nonlinear, and low signal-to-noise ratio characteristics. To address the limitations of conventional picking methods, such as low accuracy, strong manual dependence, and poor efficiency, this study proposes an intelligent microseismic signal recognition method based on energy dissipation and multiscale complexity evolution. First, variational mode decomposition is employed to perform multiscale energy decomposition of microseismic signals, and the dynamic evolution characteristics of different frequency bands are characterized from the perspective of energy dissipation and aggregation. Permutation entropy is then introduced to quantitatively evaluate the complexity of each mode, thereby enabling the adaptive suppression of modes dominated by energy dissipation. Subsequently, one-dimensional non-local means filtering is applied for energy-differentiated reconstruction to enhance the structural characteristics of the principal seismic phase. Sliding kurtosis and fractal dimension features are further extracted to construct a fractal feature-based combined kurtosis seismic phase timing identification algorithm (Fractal-PAI-K) fusion scoring function, in which abrupt complexity changes are used to characterize the transition of energy from a dissipative state to an aggregated state. Finally, high-precision automatic identification of the P-wave first arrival is achieved through dynamic thresholding. A case study based on measured microseismic data from a deep mine demonstrates that the proposed method can stably identify first-arrival times under various signal-to-noise-ratio conditions, with a mean absolute error of less than one sampling interval and a picking success rate exceeding 95%. Compared with conventional methods such as complete ensemble empirical mode decomposition with adaptive noise and wavelet thresholding, the proposed method reduces the root mean square error by up to 54.3% and improves the output signal-to-noise ratio metric by up to 82.5%. These results indicate that the collaborative analysis framework integrating energy dissipation and complexity exhibits excellent robustness under low signal-to-noise-ratio conditions, spectral overlap, and non-stationary noise, and can provide reliable technical support for microseismic signal recognition and source localization in the real-time monitoring of dynamic disasters in deep mines.
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2026, Vol. 45 
