scholarly journals Synchrosqueezing Transform for High Resolution Time-Frequency Analysis of Shock and Vibration Measurements

2016 ◽  
Vol 4 (1) ◽  
pp. 177-184
Author(s):  
Divya Choudhary ◽  
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Resolution Time ◽  
Vibration Measurements ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Synchrosqueezing Transform

High resolution time-frequency analysis of otoacoustic emissions

1997 ◽  
Vol 5 (6) ◽  
pp. 407-418 ◽  
Author(s):  
K.J. Blinowska ◽  
P.J. Durka ◽  
A. Skierski ◽  
F. Grandori ◽  
G. Tognola
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Otoacoustic Emissions ◽  
Resolution Time ◽  
Time Frequency Analysis ◽  
Time Frequency

High-resolution characterization of geologic structures using the synchrosqueezing transform

2017 ◽  
Vol 5 (1) ◽  
pp. T75-T85 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Zhuosheng Zhang ◽  
Xiudi Jiang ◽  
Qi Lv
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Proper Time ◽  
Frequency Resolution ◽  
Heisenberg Uncertainty Principle ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Basic Wavelet ◽  
Synchrosqueezing Transform

The main factors responsible for the nonstationarity of seismic signals are the nonstationarity of the geologic structural sequences and the complex pore structure. Time-frequency analysis can identify various frequency components of seismic data and reveal their time-variant features. Choosing a proper time-frequency decomposition algorithm is the key to analyze these nonstationarity signals and reveal the geologic information contained in the seismic data. According to the Heisenberg uncertainty principle, we cannot obtain the finest time location and the best frequency resolution at the same time, which results in the trade-off between the time resolution and the frequency resolution. For instance, the most commonly used approach is the short-time Fourier transform, in which the predefined window length limits the flexibility to adjust the temporal and spectral resolution at the same time. The continuous wavelet transform (CWT) produces an “adjustable” resolution of time-frequency map using dilation and translation of a basic wavelet. However, the CWT has limitations in dealing with fast varying instantaneous frequencies. The synchrosqueezing transform (SST) can improve the quality and readability of the time-frequency representation. We have developed a high-resolution and effective time-frequency analysis method to characterize geologic bodies contained in the seismic data. We named this method the SST, and the basic wavelet is the three-parameter wavelet (SST-TPW). The TPW is superior in time-frequency resolution than those of the Morlet and Ricker wavelets. Experiments on synthetic and field data determined its validity and effectiveness, which can be used in assisting in oil/gas reservoir identification.

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