Improvement in GPR coherent noise attenuation using τ‐p and wavelet transforms

Geophysics ◽  
2004 ◽  
Vol 69 (3) ◽  
pp. 789-802 ◽  
Author(s):  
Luigia Nuzzo ◽  
Tatiana Quarta
Keyword(s):  
Wavelet Transforms ◽  
Sampling Rate ◽  
Window Size ◽  
Real Data ◽  
Discrete Wavelet ◽  
Noise Attenuation ◽  
Computationally Efficient ◽  
Coherent Noise ◽  
Amplitude Noise ◽  
Time Space

We present a new application of modern filtering techniques to ground‐penetrating radar (GPR) data processing for coherent noise attenuation. We compare the performance of the discrete wavelet transform (DWT) and the linear Radon transform (τ‐p) to classical time‐space and Fourier domain methods using a synthetic model and real data. The synthetic example simulates problems such as system ringing and surface scattering, which are common in real cases. The field examples illustrate the removal of nearly horizontal but variable‐amplitude noise features. In such situations, classical space‐domain techniques require several trials before finding an appropriate averaging window size. Our comparative analysis indicates that the DWT method is better suited for local filtering than are 2D frequency‐domain (f‐f) techniques, although the latter are computationally efficient. Radon‐based methods are slightly superior than the techniques previously used for local directional filtering, but they are slow and quite sensitive to the p‐sampling rate, p‐range, and sizes of the muting zone. Our results confirm that Radon and wavelet methods are effective in removing noise from GPR images with minimal distortions of the signal.

A New Method for Characterizing Replacement Rate Variation in Molecular Sequences: Application of the Fourier and Wavelet Models to Drosophila and Mammalian Proteins

Genetics ◽  
2000 ◽  
Vol 154 (1) ◽  
pp. 381-395
Author(s):  
Pavel Morozov ◽  
Tatyana Sitnikova ◽  
Gary Churchill ◽  
Francisco José Ayala ◽  
Andrey Rzhetsky
Keyword(s):  
Wavelet Transforms ◽  
Parametric Bootstrap ◽  
Real Data ◽  
New Method ◽  
Rate Variation ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Ratio Test ◽  
Replacement Rate ◽  
New Models

Abstract We propose models for describing replacement rate variation in genes and proteins, in which the profile of relative replacement rates along the length of a given sequence is defined as a function of the site number. We consider here two types of functions, one derived from the cosine Fourier series, and the other from discrete wavelet transforms. The number of parameters used for characterizing the substitution rates along the sequences can be flexibly changed and in their most parameter-rich versions, both Fourier and wavelet models become equivalent to the unrestricted-rates model, in which each site of a sequence alignment evolves at a unique rate. When applied to a few real data sets, the new models appeared to fit data better than the discrete gamma model when compared with the Akaike information criterion and the likelihood-ratio test, although the parametric bootstrap version of the Cox test performed for one of the data sets indicated that the difference in likelihoods between the two models is not significant. The new models are applicable to testing biological hypotheses such as the statistical identity of rate variation profiles among homologous protein families. These models are also useful for determining regions in genes and proteins that evolve significantly faster or slower than the sequence average. We illustrate the application of the new method by analyzing human immunoglobulin and Drosophilid alcohol dehydrogenase sequences.

Signal extraction using randomized-order multichannel singular spectrum analysis

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V69-V84 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Yimin Yuan ◽  
Shuwei Gan ◽  
Yangkang Chen
Keyword(s):  
Spectrum Analysis ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Data Sets ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Singular Spectrum ◽  
3D Data ◽  
Time Space ◽  
Ground Roll

Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation; however, it cannot be used to suppress coherent noise. This limitation results from the fact that the conventional MSSA method cannot distinguish between useful signals and coherent noise in the singular spectrum. We have developed a randomization operator to disperse the energy of the coherent noise in the time-space domain. Furthermore, we have developed a novel algorithm for the extraction of useful signals, i.e., for simultaneous random and coherent noise attenuation, by introducing a randomization operator into the conventional MSSA algorithm. In this method, which we call randomized-order MSSA, the traces along the trajectory of each signal component are randomly rearranged. Two ways to extract the trajectories of different signal components are investigated. The first is based on picking the extrema of the upper envelopes, a method that is also constrained by local and global gradients. The second is based on dip scanning in local processing windows, also known as the Radon method. The proposed algorithm can be applied in 2D and 3D data sets to extract different coherent signal components or to attenuate ground roll and multiples. Different synthetic and field data examples demonstrate the successful performance of the proposed method.

A Motor Point Identification Technique Based on Dempster Shafer Theory

2014 ◽  
Author(s):  
Madhavi Anugolu ◽  
Chandrasekhar Potluri ◽  
Alex Urfer ◽  
Marco P. Schoen
Keyword(s):  
Wavelet Transforms ◽  
Sampling Rate ◽  
Test Subject ◽  
Discrete Wavelet ◽  
Motor Point ◽  
Point Location ◽  
Semg Signal ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Semg Signals

The objective of this work is to identify the motor point location from the obtained sEMG signals using Dempster Shafer theory (DST). The proposed technique is applied on data obtained from a male test subject. In particular, the sEMG signals and its corresponding skeletal muscle force signals from the Flexor Digitorum Superficialis are acquired at a sampling rate of 2000 Hz using a Delsys Bangnoli- 16 EMG system. The acquired sEMG signals are rectified and filtered using a Discrete Wavelet Transforms (DWT) with a Daubechies 44 mother wavelet. For the system identification, an Output Error (OE) model structure is assumed to obtain the dynamic relation between the sEMG signal and the corresponding finger force signals. Subsequently, model based probabilities and fuzzy inference based probabilities are obtained for discrete sensor locations of a sEMG sensor array. Considering these evidences, a DST based motor point location identification method is proposed. The results based on one subject show the potential of the proposed theory and approach for affectively identifying motor point locations using an array sEMG sensor.

Lateral prediction for noise attenuation by t-x and f-x techniques

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1887-1896 ◽  
Author(s):  
Ray Abma ◽  
Jon Claerbout
Keyword(s):  
Random Noise ◽  
High Amplitude ◽  
Noise Attenuation ◽  
Space Domain ◽  
Frequency Space ◽  
Amplitude Noise ◽  
Time Space ◽  
Domain Prediction ◽  
Prediction Techniques ◽  
Prediction Filter

Attenuating random noise with a prediction filter in the time‐space domain generally produces results similar to those of predictions done in the frequency‐space domain. However, in the presence of moderate‐ to high‐amplitude noise, time‐space or t-x prediction passes less random noise than does frequency‐space, or f-x prediction. The f-x prediction may also produce false events in the presence of parallel events where t-x prediction does not. These advantages of t-x prediction are the result of its ability to control the length of the prediction filter in time. An f-x prediction produces an effective t-x domain filter that is as long in time as the input data. Gulunay’s f-x domain prediction tends to bias the predictions toward the traces nearest the output trace, allowing somewhat more noise to be passed, but this bias may be overcome by modifying the system of equations used to calculate the filter. The 3-D extension to the 2-D t-x and f-x prediction techniques allows improved noise attenuation because more samples are used in the predictions, and the requirement that events be strictly linear is relaxed.

A New Strategy for Detecting Gear Faults Using Denoising With the Orthogonal Discrete Wavelet Transform (ODWT)

1999 ◽  
Author(s):  
Sidi M. Berri ◽  
J. M. Klosner
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Transmission ◽  
Discrete Wavelet ◽  
Amplitude Noise ◽  
Transmission Pair ◽  
New Strategy ◽  
Band Pass ◽  
Local Defects ◽  
Low Amplitude

Abstract This paper investigates a new strategy for early detection of defects in a power transmission pair of spur gears. Sensitivity to local defects is enhanced by processing the signal as follows. The orthogonal discrete wavelet transform (ODWT) of the band-pass filtered averaged signal is first obtained. This is followed by thresholding in the wavelet domain, thereby removing the low amplitude noise contribution. The inverse wavelet transform then essentially reconstructs the component of the signal that is due to the defect. Experimental results demonstrate the efficiency of this procedure.

Image Weight Models Based on Discrete Wavelet Transforms

2021 ◽  
Author(s):  
Maya M. Lyasheva ◽  
Stella A. Lyasheva ◽  
Mikhail P. Shleymovich
Keyword(s):  
Wavelet Transforms ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms
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