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.
Images obtained in dim light do not clearly represent the target scene, limiting information transmission on the image carrier. This study proposes a texture-preserving image enhancement method, i.e. ETPR. The proposed method draws the illumination map of a low light image by Max-RGB, and then applies the weighted median filter algorithm and Retinex to improve the illumination map further. Then, the enhanced image is changed from RGB mode to YCbCr mode and the texture of the luminance component Y is described. It is achieved by denoising the texture of the image to be enhanced, and then the final enhanced image is obtained by the method of sub-region image fusion denoising. The effectiveness of ETPR is established by comparing it with existing enhancement technologies using public data and real images.
Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.
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