Ground‐roll suppression using the wavelet transform

Geophysics ◽  
1997 ◽  
Vol 62 (6) ◽  
pp. 1896-1903 ◽  
Author(s):  
Andrew J. Deighan ◽  
Doyle R. Watts

Low‐frequency, high‐amplitude ground roll is an old problem in land‐based seismic field records. Current processing techniques aimed at ground‐roll suppression, such as frequency filtering, f-k filtering, and f-k filtering with time‐offset windowing, use the Fourier transform, a technique that assumes that the basic seismic signal is stationary. A new alternative to the Fourier transform is the wavelet transform, which decomposes a function using basis functions that, unlike the Fourier transform, have finite extent in both frequency and time. Application of a filter based on the wavelet transform to land seismic shot records suppresses ground roll in a time‐frequency sense; unlike the Fourier filter, this filter does not assume that the signal is stationary. The wavelet transform technique also allows more effective time‐frequency analysis and filtering than current processing techniques and can be implemented using an algorithm as computationally efficient as the fast Fourier transform. This new filtering technique leads to the improvement of shot records and considerably improves the final stack quality.

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Meifal Rusli

<p class="TTPParagraphothers"><em>The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed.</em> The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). <em>As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map</em><em>. </em>Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.</p>


2014 ◽  
Vol 592-594 ◽  
pp. 2091-2096
Author(s):  
H.N. Sharma ◽  
Santosh Verma

This work employs the wavelet transform for reading the fault diagnosis in a rotor-bearing system. Initiating with literature review with some relevant studies of bearing fault and the signal processing techniques used followed by the theory of wavelet transform. A bearing test rig is shown which is used for implementing wavelet transform. A faulty bearing vibration signal is measured from the test rig; thereafter the fast Fourier transform is plotted to show the critical frequencies, bearing characteristics frequency and its harmonics. A scalogram showing the energy levels of signal is plotted as result. Faulty signal is analyzed using wavelet transform.


Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. V43-V51 ◽  
Author(s):  
Wenkai Lu ◽  
Fangyu Li

The spectral decomposition technique plays an important role in reservoir characterization, for which the time-frequency distribution method is essential. The deconvolutive short-time Fourier transform (DSTFT) method achieves a superior time-frequency resolution by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive frequencies fallen in the seismic signal bandwidth only. In general, because the low-frequency components of a seismic signal are dominant, the removal of the negative frequencies may introduce a sharp edge at the zero frequency, which would produce artifacts in the DSTFT spectrogram. To avoid this problem, we used the analytic signal, which is obtained by applying the Hilbert transform on the original real seismic signal, to calculate the STFT spectrogram in our method. Synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method.


2016 ◽  
Vol 1 ◽  
Author(s):  
Meifal Rusli

<p class="TTPParagraphothers"><em>The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed.</em> The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). <em>As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map</em><em>. </em>Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.</p>


2020 ◽  
Author(s):  
Karlton Wirsing

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Mordecai F. Raji ◽  
JianPing Li ◽  
Amin Ul Haq ◽  
Victor Ejianya ◽  
Jalaluddin Khan ◽  
...  

The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Satish Chand

A new representation of the Fourier transform in terms of time and scale localization is discussed that uses a newly coinedA-wavelet transform (Grigoryan 2005). TheA-wavelet transform uses cosine- and sine-wavelet type functions, which employ, respectively, cosine and sine signals of length2π. For a given frequencyω, the cosine- and sine-wavelet type functions are evaluated at time points separated by2π/ωon the time-axis. This is a two-parameter representation of a signal in terms of time and scale (frequency), and can find out frequency contents present in the signal at any time point using less computation. In this paper, we extend this work to provide further signal information in a better way and name it asA*-wavelet transform. In our proposed work, we use cosine and sine signals defined over the time intervals, each of length2πm/(2nω),m≤2n,mandnare nonnegative integers, to develop cosine- and sine-type wavelets. Using smaller time intervals provides sharper frequency localization in the time-frequency plane as the frequency is inversely proportional to the time. It further reduces the computation for evaluating the Fourier transform at a given frequency. TheA-wavelet transform can be derived as a special case of theA*-wavelet transform.


2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.


2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.


Author(s):  
Denis Borisovich Fedosenkov ◽  
Anna Alekseevna Simikova ◽  
Boris Andreevich Fedosenkov ◽  
Stanislav Matveevich Kulakov

The article describes the development of a special approach based on using multidimensional wavelet distributions principle to monitor and control the feed dozing processes in the mix preparation unit. As a key component, this approach uses the multidimensional time-frequency Wigner-Ville distribution, which is the part of Cohen's class distributions. The research focuses on signals characterizing mass transfer processes in the form of material flow measuring signals in relevant points of the unit. Wigner-Ville distribution has been shown in time terms as Fourier transform of products of multiplied parts of the signal under consideration for past and future time moments; corresponding distribution for the frequency spectrum is shown as Fourier transform of the products of signal parts for high-frequency and low-frequency fragments of the signal spectrum. It has been noted that when using a complex model of a dozing signal, discrete values (samples) of the latter are considered as its real values. The description of the signal parameters (amplitude, phase, frequency) has been carried out with the help of Hilbert transform. In Cohen's class distributions which represent one-dimensional non-stationary flow signals, the concept of ‘instantaneous frequency’ has been introduced. A graphical explanation for the transformation of a process flow signal from a one-dimensional time domain to a time-frequency 2 D/ 3 D -space is presented. The technology of developing a multidimensional image in the form of Wigner distribution for one-dimensional signals of continuous spiral or screw-type feeders has been examined in detail. There have been considered the features to support Wigner distribution, which allow to guess the presence or absence of time-frequency distribution elements in the interval of signal recording. There has been demonstrated how Wigner distribution can be obtained for a continuous-intermittent feeding signal. It has been concluded that for a certain types of the signal for zero fragments of the latter, non-zero time-frequency elements (i.e. virtual, anomalous ones) appear on the distribution. In addition to Wigner distribution, two other distributions - of Rihachek and Page - are considered. They display the same signal and also contain virtual elements, but in different domains of the time-frequency space. A generalized multidimensional compound signal distribution with a so-called distribution kernel available in it is presented, which includes a correction parameter that allows controlling the intensity of the virtual signal energy.


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