scholarly journals THIN BED IDENTIFICATION IMPROVEMENT USING SHORT – TIME FOURIER TRANSFORM HALF – CEPSTRUM ON “TG” FIELD

PETRO ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 103
Author(s):  
Intan Andriani Putri ◽  
Awali Priyono
Keyword(s):  
Fourier Transform ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Resolution Enhancement ◽  
Frequency Component ◽  
Advanced Technology ◽  
High Frequency Component ◽  
Short Time Fourier Transform ◽  
Seismic Acquisition ◽  
Short Time

<p>Thin Bed Identification is still a difficult task even with the advanced technology of seismic acquisition. Certain high frequency component is necessary and could be obtained through resolution enhancement. Short – Time Fourier Transform Half Cepstrum (STFTHC) is performed to enhance seismic resolution thus a better separation of thin bed could be improved. Basic principal of STFTHC is to replace the frequency spectrum by its logarithm while phase spectrum remains the same. Synthetic seismic was built based on Ricker and Rayleigh criterion. They were used to test the program yielding a better separation of two interfaces under tuning thickness without creating new artifacts. The algorithm was applied to seismic data from TG field. Using post-STFTHC seismic data as input of acoustic impedance inversion, well tie correlation increases by 10% and decreases inversion analysis error by 17,5%. Several thin bed -which once could not- could be identified on acoustic impedance result.</p>

Frequency‐time decomposition of seismic data using wavelet‐based methods

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1906-1916 ◽  
Author(s):  
Avijit Chakraborty ◽  
David Okaya
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Seismic Data ◽  
Spectral Decomposition ◽  
Matching Pursuit ◽  
Frequency Resolution ◽  
Short Time Fourier Transform ◽  
Matching Pursuit Algorithm ◽  
Processing Algorithms ◽  
Short Time

Spectral analysis is an important signal processing tool for seismic data. The transformation of a seismogram into the frequency domain is the basis for a significant number of processing algorithms and interpretive methods. However, for seismograms whose frequency content vary with time, a simple 1-D (Fourier) frequency transformation is not sufficient. Improved spectral decomposition in frequency‐time (FT) space is provided by the sliding window (short time) Fourier transform, although this method suffers from the time‐ frequency resolution limitation. Recently developed transforms based on the new mathematical field of wavelet analysis bypass this resolution limitation and offer superior spectral decomposition. The continuous wavelet transform with its scale‐translation plane is conceptually best understood when contrasted to a short time Fourier transform. The discrete wavelet transform and matching pursuit algorithm are alternative wavelet transforms that map a seismogram into FT space. Decomposition into FT space of synthetic and calibrated explosive‐source seismic data suggest that the matching pursuit algorithm provides excellent spectral localization, and reflections, direct and surface waves, and artifact energy are clearly identifiable. Wavelet‐based transformations offer new opportunities for improved processing algorithms and spectral interpretation methods.

Constrained least-squares spectral analysis: Application to seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. V143-V167 ◽  
Author(s):  
Charles I. Puryear ◽  
Oleg N. Portniaguine ◽  
Carlos M. Cobos ◽  
John P. Castagna
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Seismic Data ◽  
Continuous Wavelet ◽  
Window Length ◽  
Short Time Fourier Transform ◽  
Constrained Least Squares ◽  
Time Frequency ◽  
Short Time

An inversion-based algorithm for computing the time-frequency analysis of reflection seismograms using constrained least-squares spectral analysis is formulated and applied to modeled seismic waveforms and real seismic data. The Fourier series coefficients are computed as a function of time directly by inverting a basis of truncated sinusoidal kernels for a moving time window. The method resulted in spectra that have reduced window smearing for a given window length relative to the discrete Fourier transform irrespective of window shape, and a time-frequency analysis with a combination of time and frequency resolution that is superior to the short time Fourier transform and the continuous wavelet transform. The reduction in spectral smoothing enables better determination of the spectral characteristics of interfering reflections within a short window. The degree of resolution improvement relative to the short time Fourier transform increases as window length decreases. As compared with the continuous wavelet transform, the method has greatly improved temporal resolution, particularly at low frequencies.

scholarly journals FREQUENCY COMPONENT EXTRACTION OF HEARTBEAT CUES WITH SHORT TIME FOURIER TRANSFORM (STFT)

2017 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Sumarna Sumarna ◽  
Agus Purwanto ◽  
Dyah Kurniawati Agustika
Keyword(s):  
Fourier Transform ◽  
Frequency Spectrum ◽  
Bandpass Filter ◽  
Frequency Component ◽  
Analysis Program ◽  
Short Time Fourier Transform ◽  
Matlab Program ◽  
Frequency Components ◽  
Short Time ◽  
Spectrum Graph

Abstract Electro-acoustic human heartbeat detector have been made with the main parts : (a) stetoscope (piece chest), (b) mic condenser, (c) transistor amplifier, and (d) cues analysis program with MATLAB. The frequency components that contained in heartbeat. cues have also been extracted with Short Time Fourier Transform (STFT) from 9 volunteers. The results of the analysis showed that heart rate appeared in every cue frequency spectrum with their harmony. The steps of the research were including detector instrument design, test and instrument repair, cues heartbeat recording with Sound Forge 10 program and stored in wav file ; cues breaking at the start and the end, and extraction/cues analysis using MATLAB. The MATLAB program included filter (bandpass filter with bandwidth between 0.01 – 110 Hz), cues breaking with hamming window and every part was calculated using Fourier Transform (STFT mechanism) and the result were shown in frequency spectrum graph. Keywords: frequency components extraction, heartbeat cues, Short Time Fourier Transform

scholarly journals Two Dimensional Short Time Hartley Transforms

2016 ◽  
Vol 21 (1) ◽  
pp. 41 ◽  
Author(s):  
Narasimman Sundararajan ◽  
A. Ebrahimi ◽  
Nannappa Vasudha
Keyword(s):  
Fourier Transform ◽  
Seismic Data ◽  
Statistical Properties ◽  
Two Dimensional ◽  
Short Time Fourier Transform ◽  
Hartley Transform ◽  
Hartley Transforms ◽  
The Fourier Transform ◽  
Definition Of ◽  
Short Time

The Hartley transform, as in the case of the Fourier transform, is not suitably applicable to non-stationary representations of signals whose statistical properties change as a function of time. Hence, different versions of 2-D short time Hartley transforms (STHT) are given in comparison with the short time Fourier transform (STFT). Although the two different versions of STHT defined here with their inverses are equally applicable, one of them is mathematically incorrect/incompatible due to the incorrect definition of the 2-D Hartley transform in literature. These definitions of STHTs can easily be extended to multi-dimensions. Computations of the STFT and the two versions of STHTs are illustrated based on 32 channels (traces) of synthetic seismic data consisting of 256 samples in each trace. Salient features of STHTs are incorporated. 

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

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.

An efficient short-time Fourier transform algorithm for grinding wheel condition monitoring through acoustic emission

2021 ◽  
Vol 113 (1-2) ◽  
pp. 585-603
Author(s):  
Wenderson N. Lopes ◽  
Pedro O. C. Junior ◽  
Paulo R. Aguiar ◽  
Felipe A. Alexandre ◽  
Fábio R. L. Dotto ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Grinding Wheel ◽  
Short Time Fourier Transform ◽  
Short Time
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