Abstract
Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) combined EMD_CS_ST denoising method is proposed. First, through EMD decomposition of the noise signal, a series of Intrinsic Mode Functions (IMF) from high frequency to low frequency are obtained. By calculating the correlation coefficient between each IMF and the original signal, the boundary component between the signal and the noise was identified, and the boundary component and its previous components were sparsely processed in the discrete wavelet transform domain to obtain the original sparse coefficient θ. Second, θ is filtered by ST to get the reconstruction coefficient θnew after denoising. Then, CS was used to recover and reconstruct θnew to get the denoised IMFnew component and then recombined with the remaining IMF components to get the signal after denoising. In the simulation experiment, the denoising process of EMD_CS_ST method is introduced in detail, and the denoising ability of EMD_CS_ST, DWT, EEMD, and VMD_DWT under 10 different noise levels is discussed. The signal-to-noise ratio, signal standard deviation, correlation coefficient, waveform diagram, and spectrogram before and after denoising are compared and analyzed. Moreover, the signals obtained from the underground cavern of the Shuangjiangkou hydropower station were denoised by the EMD_CS_ST method, and the signals before and after denoising were analyzed by time-frequency spectrum. These results show that the proposed method has better denoising ability.
Highlights
Microseismic (MS) monitoring technology, as an advanced dynamic disaster monitoring method, has been widely used in deep-buried tunnels [1], coal mines [2], underground chambers [3], rock slopes [4] and other engineering fields
Aiming at the shortcomings of Compressed Sensing (CS) in the denoising of MS signals, this paper proposes a compressed sensing soft-thresholding denoising method based on Empirical Mode Decomposition (EMD), which improves the denoising of CS for MS signals and improves the signal-to-noise ratio of MS signals
Step 4: Performed soft-thresholding filtering on the θ to obtain k reconstruction coefficients θnew, which are denoted by θnew = {θ1,new, θ2,new, . . . , θk,new }; Step 5: Reconstructed the reconstruction coefficient θnew by CS to obtain k IMFnew, denoted as IMFnew = {imf 1,new, imf 2,new, . . . , imfk,new }; Step 6: Recombined the k IMFnew components with the Remaining Components (RCs) to obtain the denoised signal, which is the effective MS signal x(t)new after denoising
Summary
Microseismic (MS) monitoring technology, as an advanced dynamic disaster monitoring method, has been widely used in deep-buried tunnels [1], coal mines [2], underground chambers [3], rock slopes [4] and other engineering fields. Guo et al [18] proposed a method of seismic data reconstruction based on the theory of compressed perception, which can better restore some essential traces lost in data acquisition. The EMD_CS_ST algorithm can be effectively applied to the noise reduction of elastic wave signals generated by rock mass micro-fracture in geotechnical engineering. Once the rock is micro-ruptured, the MS signal will be collected by the acceleration sensor, and the MS signal needs to be processed for noise reduction and effective data reconstruction. The signal is denoised by numerical simulation experiments, the application process of the algorithm is introduced in detail, the advantages and disadvantages of different noise reduction methods are compared, and the effect of noise reduction at different noise levels is discussed. The conclusions of this article are explained in detail
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