Observation-Driven Method Based on IIR Wiener Filter for Microseismic Data Denoising

Iqbal, Naveed; Zerguine, Azzedine; Kaka, SanLinn I.; Al-Shuhail, Abdullatif A.

doi:10.1007/s00024-018-1775-3

Cited by 30 publications

(8 citation statements)

References 49 publications

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“…Table 3 further shows that the detection accuracy and F1 score of FMSD model are higher than those of the four listed models, and the IIR filter will have time delay, and the selection of hidden layer of neural network model will also have a great impact on its detection effect, which takes more time than other models, so the detection performance of FMSD model is better. For more details on IIR Wiener filter and residual deep convolution neural network denoising methods, please refer to [13][14]. Note: TP: There is signal and there is signal when detected; TN: No signal and there is no signal when detected; FP: No signal but there is signal when detected; FN: There is signal but there is no signal when detected.…”

Section: Experiments 2: Detection Of Pulse Signals Of Different Inten...mentioning

confidence: 99%

“…The detection of weak signal in chaotic noise background is a new detection method based on catastrophe effect of nonlinear system, which can use less data to achieve lower SNR threshold under arbitrary noise background [5]. This method has become a research hotspot and an important branch of signal processing, and has a broad application prospect in communication, automation, fault diagnosis and seismic monitoring [6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Weak Pulse Signal under Chaotic Noise based on Fractional Maximum Correlation Entropy Algorithm

Shi

et al. 2022

J. Phys.: Conf. Ser.

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How to improve the detection accuracy of target weak signal is always the difficulty of signal processing. In this paper, based on fractional maximum correlation entropy algorithm and combined with the local linear model, a method for detecting weak pulse signal in chaotic noise background is proposed. Firstly, for the sensitivity of chaotic signal to initial values and short-term predictability, reconstruct the phase space of the observation signal, establish a local linear model, use the fractional maximum correlation entropy algorithm for parameter estimation, and perform a single-step prediction to obtain the prediction error. Then, in order to accurately detect the submerged weak pulse signal, a threshold is given. Finally, the simulation results show that the proposed model in this paper can effectively detect the weak pulse signal under the background of chaotic noise, and it is suitable for signals of different intensities, and the detection speed and accuracy are much better than other models.

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Section: Experiments 2: Detection Of Pulse Signals Of Different Inten...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Weak Pulse Signal under Chaotic Noise based on Fractional Maximum Correlation Entropy Algorithm

Shi

et al. 2022

J. Phys.: Conf. Ser.

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“…In the microseismic signal acquisition process, the effective signal of the microseismic waveform can be interfered with and drowned out by noise signals of different frequencies. To obtain pure microseismic signals, Iqbal et al 5 , Mousavi et al 6 , and Cecilia Dip et al 4 developed a denoising model using a conventional digital filter to achieve noise signal suppression. Iqbal et al 7 and Nasr et al 8 proposed a signal filtering method based on SVD that removes the noise signal in the monitoring signal.…”

Section: Introductionmentioning

confidence: 99%

Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

Zhang

Luo

et al. 2022

Sci Rep

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There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor aj is proposed to reflect the noise intensity, which uses the 1/2 power of the ratio of the median absolute value to the amplitude of the monitoring data to reflect the noise intensity of the wavelet detail signal and corrects the size of the denoising scale. Finally, the performance of the improved method is quantitatively evaluated in terms of the denoising quality and efficiency using the signal-to-noise ratio, root-mean-square error, sample entropy and running time.

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“…At present, there are many methods for denoising MS signals widely used. The characteristics and application scope of the widely used MS signals can be roughly divided into three categories: estimation filtering method [5,6], wavelet threshold filtering method [7,8], and adaptive time-frequency analysis method [9][10][11]. These methods have certain limitations in practical engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

Dong

et al. 2020

Applied Sciences

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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.

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Observation-Driven Method Based on IIR Wiener Filter for Microseismic Data Denoising

Cited by 30 publications

References 49 publications

Detection of Weak Pulse Signal under Chaotic Noise based on Fractional Maximum Correlation Entropy Algorithm

Detection of Weak Pulse Signal under Chaotic Noise based on Fractional Maximum Correlation Entropy Algorithm

Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

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