scholarly journals A Nonparametric Method for Automatic Denoising of Microseismic Data

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Pingan Peng ◽  
Liguan Wang
Keyword(s):  
Power Spectrum ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
P Wave ◽  
Noise Power ◽  
Microseismic Data ◽  
Mode Decomposition ◽  
Synthetic Datasets ◽  
Microseismic Signal

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.

scholarly journals Probing the high-z IGM with the hyperfine transition of 3He+

2020 ◽  
Vol 497 (1) ◽  
pp. 572-580 ◽  
Author(s):  
Shivan Khullar ◽  
Qingbo Ma ◽  
Philipp Busch ◽  
Benedetta Ciardi ◽  
Marius B Eide ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Signal To Noise Ratio ◽  
Neutral Hydrogen ◽  
Noise Power ◽  
Large Collection ◽  
X Ray ◽  
Hyperfine Transition ◽  
The One ◽  
X Ray Binaries ◽  
Insight Into

ABSTRACT The hyperfine transition of 3He+ at 3.5 cm has been thought as a probe of the high-z IGM, since it offers a unique insight into the evolution of the helium component of the gas, as well as potentially give an independent constraint on the 21 cm signal from neutral hydrogen. In this paper, we use radiative transfer simulations of reionization driven by sources such as stars, X-ray binaries, accreting black holes and shock heated interstellar medium, and simulations of a high-z quasar to characterize the signal and analyse its prospects of detection. We find that the peak of the signal lies in the range ∼1–50 μK for both environments, but while around the quasar it is always in emission, in the case of cosmic reionization a brief period of absorption is expected. As the evolution of He ii is determined by stars, we find that it is not possible to distinguish reionization histories driven by more energetic sources. On the other hand, while a bright QSO produces a signal in 21 cm that is very similar to the one from a large collection of galaxies, its signature in 3.5 cm is very peculiar and could be a powerful probe to identify the presence of the QSO. We analyse the prospects of the signal’s detectability using SKA1-mid as our reference telescope. We find that the noise power spectrum dominates over the power spectrum of the signal, although a modest signal-to-noise ratio can be obtained when the wavenumber bin width and the survey volume are sufficiently large.

scholarly journals A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

A NEW TECHNIQUE FOR THE STUDY OF SCATTER PROPAGATION IN THE TROPOSPHERE

1957 ◽  
Vol 35 (8) ◽  
pp. 823-830 ◽  
Author(s):  
J. H. Chapman ◽  
W. J. Heikkila ◽  
J. E. Hogarth
Keyword(s):  
Power Spectrum ◽  
Communication System ◽  
Carrier Frequency ◽  
Signal To Noise Ratio ◽  
Signal Strength ◽  
Noise Power ◽  
Signal To Noise ◽  
A New Technique ◽  
Conventional Signal ◽  
Noise Power Density

The power spectrum of the fluctuations in received signal strength on a near-optical U.H.F. circuit has been measured. The sidebands associated with these fluctuations can overlap the information-carrying sidebands of a communication system. When this happens, these sidebands must be taken into account in determining the signal-to-noise ratio of the system. In other words, the fluctuations then have the characteristics of noise, and therefore they are called propagation noise in the present paper. Experiments at a carrier frequency of 500 Mc. have shown that the propagation noise power density usually varies with sideband frequency ƒ (measured from the carrier) as 1/ƒ2, for f in the range 0.1 to 10 c.p.s. Departures from this law have been observed in the regions near 0.1 c.p.s. and 10 c.p.s. The measurement of the power spectrum directly offers several advantages over the conventional signal strength recording method, and these are discussed herein.

Research on Fault Diagnosis for Gear Based on Ensemble Empirical Mode Decomposition and Slice Bi-Spectrum

2013 ◽  
Vol 718-720 ◽  
pp. 934-939
Author(s):  
Gui Ji Tang ◽  
Xiao Long Wang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Ensemble Empirical Mode Decomposition ◽  
Quadratic Phase ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Signal ◽  
Harmonic Components ◽  
Simulation Signal

A new method on fault diagnosis for gear based on ensemble empirical mode decomposition and slice bi-spectrum is proposed. Firstly, fault signal was decomposed into a series of intrinsic mode function components of different frequency bands by EEMD, and then calculated the envelope signal of IMF component by Hilbert demodulation method. Finally, analyzed the envelope signal by slice bi-spectrum and extracted the fault characteristic frequency. The anti-alias decomposition capacity of EEMD and capabilities of noise suppression and non-quadratic phase coupling harmonic components elimination of slice bi-spectrum were verified by analyzing the simulation signal. The analysis results of gear pitting failure signal and gear wear fault signal showed that this method could judge gear fault type accurately and has a certainly degree reliability.

scholarly journals Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

2021 ◽  
Vol 11 (22) ◽  
pp. 10943
Author(s):  
Zhili Chen ◽  
Peng Wang ◽  
Zhixian Gui ◽  
Qinghui Mao
Keyword(s):  
Signal To Noise Ratio ◽  
Solution Process ◽  
Vertical Component ◽  
Microseismic Monitoring ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Microseismic Data ◽  
Mode Decomposition ◽  
Processing Step

Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.

scholarly journals Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion

2020 ◽  
Vol 10 (11) ◽  
pp. 3790 ◽  
Author(s):  
Jinyong Zhang ◽  
Linlu Dong ◽  
Nuwen Xu
Keyword(s):  
Empirical Mode Decomposition ◽  
Akaike Information Criterion ◽  
Noise Suppression ◽  
Information Criterion ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition

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.

scholarly journals Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research

2022 ◽  
Vol 16 ◽  
pp. 509-516
Author(s):  
Wei Li ◽  
Wei Hu ◽  
Kun Hu ◽  
Qiang Qin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Electrical Signal ◽  
Signal Acquisition ◽  
Semg Signal ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Human Muscles ◽  
Eemd Method

The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.

scholarly journals Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3133
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Ryan Wen Liu ◽  
Pandian Vasant
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Ensemble Empirical Mode Decomposition ◽  
Wavelet Packet Transform ◽  
Smart Meters ◽  
Model Decomposition ◽  
Mode Decomposition ◽  
Data Heterogeneity ◽  
Electricity Load ◽  
Load Disaggregation

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

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