The Single-Channel Microseismic Mine Signal Denoising Method and Application Based on Frequency Domain Singular Value Decomposition (FSVD)

Quanjie Zhu (), Longkun Sui, Qingsong Li, Yage Li, Lei Gu and Dacang Wang
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Quanjie Zhu: School of Emergency Technology and Management, North China Institute of Science and Technology, Langfang 065201, China
Longkun Sui: School of Mine Safety, North China Institute of Science and Technology, Langfang 065201, China
Qingsong Li: Guizhou Coal Mine Design & Research Institute, Guiyang 550025, China
Yage Li: Science and Technology Innovation Department, China National Coal Group Corp, Beijing 100120, China
Lei Gu: School of Mine Safety, North China Institute of Science and Technology, Langfang 065201, China
Dacang Wang: School of Mine Safety, North China Institute of Science and Technology, Langfang 065201, China

Sustainability, 2023, vol. 15, issue 13, 1-22

Abstract: The purpose of denoising microseismic mine signals (MMS) is to extract relevant signals from background interference, enabling their utilization in wave classification, identification, time analysis, location calculations, and detailed mining feature analysis, among other applications. To enhance the signal-to-noise ratio ( SNR ) of single-channel MMS, a frequency-domain denoising method based on the Fourier transform, inverse transform, and singular value decomposition was proposed, along with its processing workflow. The establishment of key parameters, such as time delay, τ , reconstruction order, k , Hankel matrix length, n , and dimension, m, were introduced. The reconstruction order for SVD was determined by introducing the energy difference spectrum, E, and the denoised two-dimensional microseismic time series was obtained based on the SVD recovery principle. Through the analysis and processing of three types of typical microseismic waveforms in mining (blast, rock burst, and background noise) and with the evaluation of four indicators, SNR , ESN , RMSE , and STI , the results show that the SNR is improved by more than 10 dB after FSVD processing, indicating a strong noise suppression capability. This method is of significant importance for the rapid analysis and processing of microseismic signals in mining, as well as subsequently and accurately picking the initial arrival times and the exploration and analysis of microseismic signal characteristics in mines.

Keywords: microseismic signal; noise reduction; singular value decomposition; Fourier transform; arrival time picking (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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