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

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.

Application of 2D Variational Mode Decomposition Method in Seismic Signal Denoising

“…Geetha, Hota, and Karras [8] used honey badger optimisation algorithm to optimise the wavelet transform method; compared with the traditional denoising method, the denoising effect of this method is more obvious. Zhu, Sui, Li, Li, Gu, and Wang [9] used frequency domain singular value decomposition (FSVD) to denoise the microseismic mine signal. The results show that the signal-to-noise ratio is improved by more than 10 dB after FSVD processing.…”