Wavelet denoising techniques with applications to experimental geophysical data

To, Albert C.; Moore, Jeffrey R.; Glaser, Steven D.

doi:10.1016/j.sigpro.2008.07.023

Cited by 93 publications

(49 citation statements)

References 23 publications

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“…Wavelet transform has the specialty of multi-scale, low entropy and decorrelation. It is good at removing random noise and has become a conventional method in signal processing [36]. The multiscale feature of wavelet transform make it has strong local recognition ability.…”

Section: Target Detection Preprocessing Algorithmmentioning

confidence: 99%

Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter

Xing

Yan

2018

Complexity

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In order to detect low-flying small targets in complex sea condition effectively, we study the chaotic characteristic of sea clutter, use joint algorithm combined complete ensemble empirical mode decomposition (CEEMD) with wavelet transform to de-noise, and put forward a detection method for low-flying target under the sea clutter background based on Volterra filter. By CEEMD method, sea clutter signal which contains small target can be decomposed into a series of intrinsic mode function (IMF) components, pick out high-frequency components which contain more noise by autocorrelation function, and perform wavelet transform on them. The de-noised components and remaining components are used to reconstruct clear signal. In view of the chaotic characteristics of sea clutter, we use Volterra filter to establish adaptive prediction model, detect low-flying small target hiding in sea clutter background from the prediction error, and compare the root mean square error (RMSE) before and after de-noising to evaluate de-noising effect. Experimental results show that the joint algorithm can effectively remove noise and reduce the RMSE by 40% at least. Volterra prediction model can directly detect low-flying small target under sea clutter background from the prediction error in the cases of high signal-to-noise ratio (SNR). In the cases of low SNR, after de-noised by joint algorithm, Volterra prediction model can also detect the low-flying small target clearly.

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Section: Target Detection Preprocessing Algorithmmentioning

confidence: 99%

Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter

Xing

Yan

2018

Complexity

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“…Han and van der Baan [10] developed a novel seismic and microseismic denoising method based on EEMD combined with adaptive thresholding. To et al [20] compared Fourier-based and wavelet-based denoising techniques applied to geophysical data. Gaci [21] studied soft thresholding denoising techniques based on the discrete wavelet transform to enhance the firstarrival picking.…”

Section: Introductionmentioning

confidence: 99%

A Nonparametric Method for Automatic Denoising of Microseismic Data

Peng

Wang

2018

Shock and Vibration

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Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data. The method consists of three major steps: (1) applying a two-step AIC algorithm to pick P-wave arrival; (2) subtracting the noise power spectrum from the signal power spectrum; (3) recovering the microseismic signal by inverse Fourier transform. The proposed method is tested on synthetic datasets with different signal types and SNRs, as well as field datasets. The results of the proposed method are compared against ensemble empirical mode decomposition (EEMD) and wavelet denoising methods, which shows the effectiveness of the method for denoising and improving the SNR of microseismic data.

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“…Noise in a signal is impossible to eliminate completely because of the superposition of the redundant noise frequency and the effective wave frequency [18,19]. Regarding frequency superposition, two challenges must be faced.…”

Section: Introductionmentioning

confidence: 99%

A Synthetic Solution for Identification and Extraction of the Effective Microseismic Wave Component Using Decomposition on Time, Frequency, and Wavelet Coefficient Domains

Zhang

Meng

Liu

et al. 2017

Shock and Vibration

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To reduce noise components from original microseismic waves, a comprehensive fine signal processing approach using the integrated decomposition analysis of the wave duration, frequency spectrum, and wavelet coefficient domain was developed and implemented. Distribution regularities of the wave component and redundant noise on the frequency spectrum and the wavelet coefficient domain were first expounded. The frequency threshold and wavelet coefficient threshold were determined for the identification and extraction of the effective wave component. The frequency components between the reconstructed microseismic wave and the original measuring signal were compared. The noise elimination effect via the scale-changed domain decomposition was evaluated. Interaction between the frequency threshold and the wavelet coefficient threshold in the time domain was discussed. The findings reveal that tri-domain decomposition analysis achieves the precise identification and extraction of the effective microseismic wave component and improves the reliability of waves by eliminating the redundant noise. The frequency threshold and the wavelet coefficient threshold on a specific time window are two critical parameters that determine the degree of precision for the identification of the extracted wave component. This research involves development of the proposed integrated domain decomposition method and provides a diverse view on the fine processing of the microseismic signal.

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Wavelet denoising techniques with applications to experimental geophysical data

Cited by 93 publications

References 23 publications

Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter

Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter

A Nonparametric Method for Automatic Denoising of Microseismic Data

A Synthetic Solution for Identification and Extraction of the Effective Microseismic Wave Component Using Decomposition on Time, Frequency, and Wavelet Coefficient Domains

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