Automatic Picking Method of Microseismic P-Wave First Arrival Based on SST and Fractal Local Box-Counting Dimension Denoising with CF-AIC
- 1College of Energy and Mining Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
- 2Key Laboratory of Western Mines and Hazard Prevention of Ministry of Education, Xi’an University of Science and Technology, Xi’an 710054, China
- 3College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Abstract
Microseismic monitoring is a crucial technical approach for deep underground engineering and the early warning of dynamic disasters. However, the high-precision automatic picking of microseismic P-wave first arrivals under strong non-stationary background noise remains a critical challenge in this field. To address the limitations of conventional time-frequency denoising methods, in which hard thresholding may oversmooth the onset edges of first arrivals and consequently cause STA/LTA or global AIC algorithms to be trapped by noise-induced local extrema, this paper proposes a novel automatic P-wave picking method. This method relies on the synchrosqueezing transform (SST) and local box-counting dimension for denoising, integrating permutation entropy with the Akaike Information Criterion (AIC). During the denoising stage, a dual-discrimination mechanism combines SST-based time-frequency component selection with local box-counting dimension analysis. Initially, candidate high-energy time-frequency points are selected from the SST representation using adaptive thresholds, while the remaining components are attenuated to reduce diffuse background noise. Subsequently, the local box-counting dimension of the continuous time-frequency plane is calculated to characterize the differences in the time-frequency fractal structure of the signal. Based on this, an asymmetric fractal gain operator is constructed to suppress noise disturbances while providing directional energy compensation for the first-arrival jump edges, thereby mitigating the smoothing distortion of weak waveforms during the denoising process. In the picking stage, the permutation entropy and variance features of the local waveforms are extracted to formulate a joint feature function for locating candidate onset regions. This is combined with a sliding AIC to perform a localized fine search, overcoming the vulnerability of traditional global searches to noise extrema interference and achieving high-precision localization of P-wave first arrivals. Verification using real mine data demonstrates that the proposed method significantly enhances signal quality under complex background noise conditions while effectively preserving the initial onset characteristics of the seismic phases. Under simulated input SNRs of 20, 10, and 0 dB, the proposed method achieves SNR gains of 17.22, 15.98, and 10.54 dB, respectively, corresponding to a mean gain of 14.58. The method also yields the lowest RMSE among the compared denoising methods at input SNRs of 10 and 0 dB. The research results indicate that this method can provide reliable technical support for the accurate picking and source localization of microseismic events in highly noisy environments.
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BibTeX
@article{li2026automatic,
title = {{Automatic Picking Method of Microseismic P-Wave First Arrival Based on SST and Fractal Local Box-Counting Dimension Denoising with CF-AIC}},
author = {Leyang Li and Xingping Lai and Yuchen Hou and Pengfei Shan and Huicong Xu and Kai Li and Zhaohang Li and Cheng Lu and Yonghang Cai and Huien Li},
journal = {Fractal and Fractional},
year = {2026},
month = nov,
doi = {10.3390/fractalfract10110769},
url = {https://www.mdpi.com/2504-3110/10/11/769},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Automatic Picking Method of Microseismic P-Wave First Arrival Based on SST and Fractal Local Box-Counting Dimension Denoising with CF-AIC
AU - Leyang Li
AU - Xingping Lai
AU - Yuchen Hou
AU - Pengfei Shan
AU - Huicong Xu
AU - Kai Li
AU - Zhaohang Li
AU - Cheng Lu
AU - Yonghang Cai
AU - Huien Li
PY - 2026
DA - 2026/11/02
JO - Fractal and Fractional
DO - 10.3390/fractalfract10110769
UR - https://www.mdpi.com/2504-3110/10/11/769
AB - Microseismic monitoring is a crucial technical approach for deep underground engineering and the early warning of dynamic disasters. However, the high-precision automatic picking of microseismic P-wave first arrivals under strong non-stationary background noise remains a critical challenge in this field. To address the limitations of conventional time-frequency denoising methods, in which hard thresholding may oversmooth the onset edges of first arrivals and consequently cause STA/LTA or global AIC algorithms to be trapped by noise-induced local extrema, this paper proposes a novel automatic P-wave picking method. This method relies on the synchrosqueezing transform (SST) and local box-counting dimension for denoising, integrating permutation entropy with the Akaike Information Criterion (AIC). During the denoising stage, a dual-discrimination mechanism combines SST-based time-frequency component selection with local box-counting dimension analysis. Initially, candidate high-energy time-frequency points are selected from the SST representation using adaptive thresholds, while the remaining components are attenuated to reduce diffuse background noise. Subsequently, the local box-counting dimension of the continuous time-frequency plane is calculated to characterize the differences in the time-frequency fractal structure of the signal. Based on this, an asymmetric fractal gain operator is constructed to suppress noise disturbances while providing directional energy compensation for the first-arrival jump edges, thereby mitigating the smoothing distortion of weak waveforms during the denoising process. In the picking stage, the permutation entropy and variance features of the local waveforms are extracted to formulate a joint feature function for locating candidate onset regions. This is combined with a sliding AIC to perform a localized fine search, overcoming the vulnerability of traditional global searches to noise extrema interference and achieving high-precision localization of P-wave first arrivals. Verification using real mine data demonstrates that the proposed method significantly enhances signal quality under complex background noise conditions while effectively preserving the initial onset characteristics of the seismic phases. Under simulated input SNRs of 20, 10, and 0 dB, the proposed method achieves SNR gains of 17.22, 15.98, and 10.54 dB, respectively, corresponding to a mean gain of 14.58. The method also yields the lowest RMSE among the compared denoising methods at input SNRs of 10 and 0 dB. The research results indicate that this method can provide reliable technical support for the accurate picking and source localization of microseismic events in highly noisy environments.
SN - 2504-3110
ER -