scholarly journals Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain

2021 ◽  
Author(s):  
Hamzeh Mohammadigheymasi ◽  
Paul crocker ◽  
Maryam Fathi ◽  
Eduardo Almeida ◽  
Graça Silveira ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Seismic Waves ◽  
Eigenvalue Decomposition ◽  
Fourier Space ◽  
Polarization Analysis ◽  
Frequency Filtering ◽  
New Approach ◽  
Time Frequency ◽  
Frequency Representation ◽  
Dependent Polarization

In this paper, we present a new approach to the TF-domain PA methods. More precisely, we provide an in-detailed discussion on rearranging the eigenvalue decomposition polarization analysis (EDPA) formalism in the frequency domain to obtain the frequency-dependent polarization properties from the Fourier coefficients owing to the Fourier space orthogonality. Then, by extending the formulation to the TF-domain and incorporating sparsity-promoting time-frequency representation (SP-TFR), we alleviate the limited resolution when estimating the TFdomain polarization parameters. The final details of the technique are to apply an adaptive sparsity-promoting time-frequency filtering (SP-TFF) to extract and filter different phases of the seismic wave. By processing earthquake waveforms, we show that by combining amplitude, directivity, and rectilinearity attributes on the sparse TF-domain polarization map of the signal, we are able to extract or filter different phases of seismic waves.

scholarly journals Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain

2021 ◽  
Author(s):  
Hamzeh Mohammadigheymasi ◽  
Paul crocker ◽  
Maryam Fathi ◽  
Eduardo Almeida ◽  
Graça Silveira ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Seismic Waves ◽  
Eigenvalue Decomposition ◽  
Fourier Space ◽  
Polarization Analysis ◽  
Frequency Filtering ◽  
New Approach ◽  
Time Frequency ◽  
Frequency Representation ◽  
Dependent Polarization

In this paper, we present a new approach to the TF-domain PA methods. More precisely, we provide an in-detailed discussion on rearranging the eigenvalue decomposition polarization analysis (EDPA) formalism in the frequency domain to obtain the frequency-dependent polarization properties from the Fourier coefficients owing to the Fourier space orthogonality. Then, by extending the formulation to the TF-domain and incorporating sparsity-promoting time-frequency representation (SP-TFR), we alleviate the limited resolution when estimating the TFdomain polarization parameters. The final details of the technique are to apply an adaptive sparsity-promoting time-frequency filtering (SP-TFF) to extract and filter different phases of the seismic wave. By processing earthquake waveforms, we show that by combining amplitude, directivity, and rectilinearity attributes on the sparse TF-domain polarization map of the signal, we are able to extract or filter different phases of seismic waves.

Eigenvalue decomposition polarization analysis: A regularized sparsity-based approach

2021 ◽  
Author(s):  
Eduardo Almeida ◽  
Hamzeh Mohammadigheymasi ◽  
Maryam Fathi ◽  
Paul Crocker ◽  
Graça Silveira
Keyword(s):  
Frequency Domain ◽  
Decomposition Methods ◽  
The Body ◽  
Eigenvalue Decomposition ◽  
Seismic Signals ◽  
Polarization Analysis ◽  
Technical Program ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Dependent Polarization

<p>Polarization analysis is a signal processing tool for decomposing multi-component seismic signals to a set of rectilinearly or elliptically polarized elements. Theoretically, time-frequency polarization methods are the most compatible tool to analyze the intrinsically non-stationary seismic signals. They decompose the signal to a superposition of well-defined polarized elements, localized in the time and frequency domains. However, in practice, they suffer from instability and limited resolution for discriminating between interfering seismic phases in time and frequency, as the time-frequency decomposition methods are generally an underdetermined mapping from the time to the time-frequency domain. Our contribution is threefold: Firstly we obtain the frequency-dependent polarization properties in terms of the eigenvalue decomposition of the Fourier spectra of three-components of the signal. Secondly, by extending from the frequency to the time-frequency domain and using the regularized sparsity-based time-frequency decomposition (Portniaguine and Castagna, 2004) we are able to increase resolution and reduce instability in the presence of noise. Finally, by combining directivity, rectilineary, and amplitude attributes in the time-frequency domain, we extend the time-frequency polarization analysis to extract and filter different seismic phases. By applying this method on synthetic and real seismograms we demonstrate the efficacy of the method in discriminating between the interfering seismic phases in time and frequency, including the body, Rayleigh, Love, and coda waves. This research contributes to the FCT-funded SHAZAM (Ref. PTDC/CTA-GEO/31475/2017) project.<br><br><strong>REFERENCES</strong><br>Portniaguine, O., and J. Castagna, 2004, Inverse spectral decomposition, in SEG Technical Program Expanded Abstracts 2004: Society of Exploration Geophysicists, 1786–1789.</p>

Design of Adaptive Filter for Health Monitoring on a Gearbox

2006 ◽  
Vol 321-323 ◽  
pp. 1237-1240
Author(s):  
Sang Kwon Lee ◽  
Jung Soo Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Adaptive Filter ◽  
Health Monitoring ◽  
Background Noise ◽  
Adaptive Signal Processing ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Impulsive vibration signals in gearbox are often associated with faults, which lead to due to irregular impacting. Thus these impulsive vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive signal embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within laboratory gearbox.

scholarly journals EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2739 ◽  
Author(s):  
Rami Alazrai ◽  
Rasha Homoud ◽  
Hisham Alwanni ◽  
Mohammad Daoud
Keyword(s):  
Emotion Recognition ◽  
Frequency Domain ◽  
Frequency Distribution ◽  
Support Vector ◽  
Eeg Signals ◽  
Time Frequency ◽  
Labeling Schemes ◽  
Frequency Representation ◽  
Frequency Features ◽  
Time Frequency Distribution

Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73 . 8 % – 86 . 2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

scholarly journals Applications of an Improved Time-Frequency Filtering Algorithm to Signal Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Junbo Long ◽  
Haibin Wang ◽  
Daifeng Zha ◽  
Hongshe Fan ◽  
Zefeng Lao ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Stable Distribution ◽  
Signal Reconstruction ◽  
Order Moment ◽  
Frequency Filtering ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Noise Environment ◽  
Frequency Representation ◽  
Short Time

The short time Fourier transform time-frequency representation (STFT-TFR) method degenerates, and the corresponding short time Fourier transform time-frequency filtering (STFT-TFF) method fails underαstable distribution noise environment. A fractional low order short time Fourier transform (FLOSTFT) which takes advantage of fractionalporder moment is proposed forαstable distribution noise environment, and the corresponding FLOSTFT time-frequency representation (FLOSTFT-TFR) algorithm is presented in this paper. We study vector formulation of the FLOSTFT and inverse FLOSTFT (IFLOSTFT) methods and propose a FLOSTFT time-frequency filtering (FLOSTFT-TFF) method which takes advantage of time-frequency localized spectra of the signal in time-frequency domain. The simulation results show that, employing the FLOSTFT-TFR method and the FLOSTFT-TFF method with an adaptive weight function, time-frequency distribution of the signals can be better gotten and time-frequency localized region of the signal can be effectively extracted fromαstable distribution noise, and also the original signal can be restored employing the IFLOSTFT method. Their performances are better than the STFT-TFR and STFT-TFF methods, and MSEs are smaller in differentαand GSNR cases. Finally, we apply the FLOSTFT-TFR and FLOSTFT-TFF methods to extract fault features of the bearing outer race fault signal and restore the original fault signal fromαstable distribution noise; the experimental results illustrate their performances.

Sign in / Sign up

Export Citation Format

Share Document