scholarly journals Application of improved time–frequency representation to local noise removal

2021 ◽  
Vol 11 (5) ◽  
pp. 2091-2096
Author(s):  
Baotong Liu ◽  
Qiyuan Liu ◽  
Xuefu Kang
Keyword(s):  
Temporal Resolution ◽  
Real Data ◽  
Noise Removal ◽  
Coherent Noise ◽  
Exponential Factor ◽  
Time Frequency ◽  
Coupling Noise ◽  
Frequency Representation ◽  
S Transform ◽  
Parameter Values

AbstractThe temporal resolution of conventional S transform (ST) is not sufficient for the separation of local coherent noise. We present a revised S transform (RST) which uses an analyzing window function with two control parameters of the scalar σ and the exponential factor γ. Selecting proper parameter values (say σ = 1.1, γ = 1.08), the time–frequency representation (TFR) acquired by our method exhibits a higher temporal resolution. Applying an appropriate filter in the time–frequency domain, we are able to remove specific local noise. Distributed acoustic sensing (DAS) VSP section may suffer from fiber cable coupling noise, hindering the subsequent data processing and geologic interpretation. The real data example shows the coupling noise occurred in the DAS VSP can be removed by the presented RST.

A Time–Frequency Representation Model for Seismic Ground Motions

2020 ◽  
Author(s):  
Xi Zhong Cui ◽  
Han Ping Hong
Keyword(s):  
Ground Motions ◽  
Seismic Source ◽  
Model Parameters ◽  
Time Frequency ◽  
Motion Models ◽  
Power Spectral ◽  
Frequency Representation ◽  
Site Characteristics ◽  
S Transform ◽  
Proposed Model

ABSTRACT A probabilistic model of the time–frequency power spectral density (TFPSD) is presented. The model is developed, based on the time–frequency representation of records from strike-slip earthquakes, in which the time–frequency representation is obtained by applying the S-transform (ST). The model for the TFPSD implicitly considers the amplitude modulation and frequency modulation for the nonstationary ground motions; this differs from the commonly used evolutionary PSD model. Predicting models for the model parameters, based on seismic source and site characteristics, are developed. The use of the model to simulate ground motions for scenario seismic events is illustrated, in which the simulation is carried out using a recently developed model that is based on the discrete orthonormal ST and ST. The illustrative example highlights the simplicity of using the proposed model and the physical meaning of some of the model parameters. A model validation analysis is carried out by comparing the statistics of the pseudospectral acceleration obtained from the simulated records to those obtained using a few ground-motion models available in the literature and considered actual records. The comparison indicates the adequacy of the proposed model.

Performance Comparison of VSI Switches Faults Analysis Using STFT and S Transform

2015 ◽  
Vol 785 ◽  
pp. 210-214 ◽  
Author(s):  
M. Manap ◽  
A.R. Abdullah ◽  
N.Z. Saharuddin ◽  
N.A. Abidullah ◽  
Nur Sumayyah Ahmad ◽  
...  
Keyword(s):  
Power Converter ◽  
Performance Comparison ◽  
Voltage Source ◽  
Percentage Error ◽  
Identification System ◽  
Time Frequency ◽  
Signal Parameters ◽  
Detection And Identification ◽  
Frequency Representation ◽  
S Transform

Switches fault in power converter has become compelling issues over the years. To reduce cost and maintenance downtime, a good fault detection technique is an essential. In this paper, the performance of STFT and S transform techniques are analysed and compared for voltage source inverter (VSI) switches faults. The signal from phase current is represented in jointly time-frequency representation (TFR) to estimate signal parameters and characteristics. Then, the degree of accuracy for both STFT and S transform are determined by the lowest value of mean absolute percentage error (MAPE). The results demonstrate that S transform gives better accuracy compare to STFT and is suitable for VSI switches faults detection and identification system.

Noise removal by migration of time-shift images

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. S105-S111 ◽  
Author(s):  
Sheng Xu ◽  
Feng Chen ◽  
Bing Tang ◽  
Gilles Lambare
Keyword(s):  
Time Lag ◽  
Real Data ◽  
Velocity Model ◽  
Noise Removal ◽  
Time Shift ◽  
Reverse Time ◽  
Coherent Noise ◽  
Data Set ◽  
Depth Migration ◽  
Time Migration

When using seismic data to image complex structures, the reverse time migration (RTM) algorithm generally provides the best results when the velocity model is accurate. With an inexact model, moveouts appear in common image gathers (CIGs), which are either in the surface offset domain or in subsurface angle domain; thus, the stacked image is not well focused. In extended image gathers, the strongest energy of a seismic event may occur at non-zero-lag in time-shift or offset-shift gathers. Based on the operation of RTM images produced by the time-shift imaging condition, the non-zero-lag time-shift images exhibit a spatial shift; we propose an approach to correct them by a second pass of migration similar to zero-offset depth migration; the proposed approach is based on the local poststack depth migration assumption. After the proposed second-pass migration, the time-shift CIGs appear to be flat and can be stacked. The stack enhances the energy of seismic events that are defocused at zero time lag due to the inaccuracy of the model, even though the new focused events stay at the previous positions, which might deviate from the true positions of seismic reflection. With the stack, our proposed approach is also able to attenuate the long-wavelength RTM artifacts. In the case of tilted transverse isotropic migration, we propose a scheme to defocus the coherent noise, such as migration artifacts from residual multiples, by applying the original migration velocity model along the symmetry axis but with different anisotropic parameters in the second pass of migration. We demonstrate that our approach is effective to attenuate the coherent noise at subsalt area with two synthetic data sets and one real data set from the Gulf of Mexico.

Investigation of generalized S-transform analysis windows for time-frequency analysis of seismic reflection data

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V235-V247 ◽  
Author(s):  
Duan Li ◽  
John Castagna ◽  
Gennady Goloshubin
Keyword(s):  
Temporal Resolution ◽  
Seismic Reflection ◽  
Frequency Resolution ◽  
Low Frequencies ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Gaussian Window ◽  
Reflection Data ◽  
S Transform ◽  
Generalized S Transform

The frequency-dependent width of the Gaussian window function used in the S-transform may not be ideal for all applications. In particular, in seismic reflection prospecting, the temporal resolution of the resulting S-transform time-frequency spectrum at low frequencies may not be sufficient for certain seismic interpretation purposes. A simple parameterization of the generalized S-transform overcomes the drawback of poor temporal resolution at low frequencies inherent in the S-transform, at the necessary expense of reduced frequency resolution. This is accomplished by replacing the frequency variable in the Gaussian window with a linear function containing two coefficients that control resolution variation with frequency. The linear coefficients can be directly calculated by selecting desired temporal resolution at two frequencies. The resulting transform conserves energy and is readily invertible by an inverse Fourier transform. This modification of the S-transform, when applied to synthetic and real seismic data, exhibits improved temporal resolution relative to the S-transform and improved resolution control as compared with other generalized S-transform window functions.

scholarly journals K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

2021 ◽  
Vol 9 (6) ◽  
pp. 2650-2657
Author(s):  
Mohd Hatta Jopri ◽  
Mohd Ruddin Ab Ghani ◽  
Abdul Rahim Abdullah ◽  
Mustafa Manap ◽  
Tole Sutikno ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Single Point ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
System A ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Source

This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.

High-resolution seismic complex trace analysis by adaptive fast sparse S-transform

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. V51-V67 ◽  
Author(s):  
Hamid Sattari
Keyword(s):  
High Resolution ◽  
Hilbert Transform ◽  
Trace Analysis ◽  
Spectral Decomposition ◽  
Decomposition Method ◽  
Random Noise ◽  
Real Data ◽  
Mixed Norm ◽  
Time Frequency ◽  
S Transform

Complex trace analysis provides seismic interpreters with a view to identify the nature of challenging subsurface geologic features. However, the conventional procedure based on the Hilbert transform (HT) is highly sensitive to random noise and sudden frequency variations in seismic data. Generally, conventional filtering methods reduce the spectral bandwidth while stabilizing complex trace analysis, whereas obtaining high-resolution images of multiple thin-bed layers requires wideband data. It is thus a challenging problem to reconcile the conflict between the two purposes, and a powerful signal processing device is required. To overcome the issue, I first introduced the fast sparse S-transform (ST) as a powerful time-frequency decomposition method to improve the windowed Hilbert transform (WHT). Then, in addition to the mixed-norm higher resolution provided by the fast sparse ST, I have developed a novel sparsity-based optimization for window parameters. The process adaptively regularizes sudden changes in frequency content of nonstationary signals with the same computational complexity of the nonoptimized algorithm. The performance of the proposed windowing optimization is compared with those of available methods that have so far been used for adaptivity enhancement of Fourier-based spectral decomposition methods. The final adaptive and sparse version of WHT is used to achieve high-resolution complex trace analysis and address the above-mentioned conflict. The instantaneous complex attributes obtained by the proposed method for several synthetic and real data sets of which multiple thin-bed layers contain wedges, trapped gas reservoirs, and faults are superior to those obtained by WHT via adaptive sparse STFT, robust adaptive WHT, and conventional HT. Potential applications of the adaptive double-sparse ST as a new spectral decomposition method were also evaluated.

scholarly journals Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

2020 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Mohd Hatta Jopri ◽  
Abdul Rahim Abdullah ◽  
Jingwei Too ◽  
Tole Sutikno ◽  
Srete Nikolovski ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Current Source ◽  
Naïve Bayes ◽  
Support Vector ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Voltage ◽  
Harmonic Source

<span>A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and Naïve Bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.</span>

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

A SIGNAL-TUNED GABOR TRANSFORM WITH APPLICATION TO EEG ANALYSIS

2013 ◽  
Vol 24 (04) ◽  
pp. 1350017 ◽  
Author(s):  
JOSÉ R. A. TORREÃO ◽  
SILVIA M. C. VICTER ◽  
JOÃO L. FERNANDES
Keyword(s):  
Experimental Study ◽  
Fourier Transform ◽  
Input Signal ◽  
Fourier Component ◽  
Gabor Transform ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
The Fourier Transform

We introduce a time-frequency transform based on Gabor functions whose parameters are given by the Fourier transform of the analyzed signal. At any given frequency, the width and the phase of the Gabor function are obtained, respectively, from the magnitude and the phase of the signal's corresponding Fourier component, yielding an analyzing kernel which is a representation of the signal's content at that particular frequency. The resulting Gabor transform tunes itself to the input signal, allowing the accurate detection of time and frequency events, even in situations where the traditional Gabor and S-transform approaches tend to fail. This is the case, for instance, when considering the time-frequency representation of electroencephalogram traces (EEG) of epileptic subjects, as illustrated by the experimental study presented here.

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