Time-frequency analysis of deep crustal reflection seismic data using Wigner-Ville distributions

2001 ◽  
Vol 38 (7) ◽  
pp. 1027-1035 ◽  
Author(s):  
Kris Vasudevan ◽  
Frederick A Cook
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Drill Hole ◽  
Lateral Variation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Reflection Seismic ◽  
Instantaneous Frequencies ◽  
Crustal Reflection

One important component of deep crustal reflection seismic data in the absence of drill-hole data and surface-outcrop constraints is classifying and quantifying reflectivity patterns. One approach to this component uses a recently developed data-decomposition technique, seismic skeletonization. Skeletonized coherent events and their attributes are identified and stored in a relational database, allowing easy visualization and parameterization of the reflected wavefield. Because one useful attribute, the instantaneous frequency, is difficult to derive within the current framework of skeletonization, time–frequency analysis and a new method, empirical mode skeletonization, are used to derive it. Other attributes related to time–frequency analysis that can be derived from the methods can be used for shallow and deep reflection seismic interpretation and can supplement the seismic attributes accrued from seismic skeletonization. Bright reflections observed from below the sedimentary basin in the Southern Alberta Lithosphere Transect have recently been interpreted to be caused by highly reflective sills. Time–frequency analysis of one of these reflections shows the lateral variation of energy with instantaneous frequency for any given time and the lateral variation of energy with time for any instantaneous frequency. Results from empirical mode skeletonization for the same segment of data illustrate the differences in the instantaneous frequencies among the intrinsic modes of the data. Thus, time–frequency distribution of amplitude or energy for any signal may be a good indicator of compositional differences that can vary from one location to another.

Wavelet transform with generalized beta wavelets for seismic time-frequency analysis

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. O47-O56 ◽  
Author(s):  
Zhiguo Wang ◽  
Bing Zhang ◽  
Jinghuai Gao ◽  
Qingzhen Wang ◽  
Qing Huo Liu
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Significant Information ◽  
Trade Off ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Reflection Seismic ◽  
Seismic Waveform ◽  
Frequency Representation

Using the continuous wavelet transform (CWT), the time-frequency analysis of reflection seismic data can provide significant information to delineate subsurface reservoirs. However, CWT is limited by the Heisenberg uncertainty principle, with a trade-off between time and frequency localizations. Meanwhile, the mother wavelet should be adapted to the real seismic waveform. Therefore, for a reflection seismic signal, we have developed a progressive wavelet family that is referred to as generalized beta wavelets (GBWs). By varying two parameters controlling the wavelet shapes, the time-frequency representation of GBWs can be given sufficient flexibility while remaining exactly analytic. To achieve an adaptive trade-off between time-frequency localizations, an optimization workflow is designed to estimate suitable parameters of GBWs in the time-frequency analysis of seismic data. For noise-free and noisy synthetic signals from a depositional cycle model, the results of spectral component using CWT with GBWs display its flexibility and robustness in the adaptive time-frequency representation. Finally, we have applied CWT with GBWs on 3D seismic data to show its potential to discriminate stacked fluvial channels in the vertical sections and to delineate more distinct fluvial channels in the horizontal slices. CWT with GBWs provides a potential technique to improve the resolution of exploration seismic interpretation.

scholarly journals A Time-Frequency Research for Ultrasonic Guided Wave Generated from the Debonding Based on a Novel Time-Frequency Analysis Technique

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Junhua Wu ◽  
Zheshu Ma ◽  
Yonghui Zhang
Keyword(s):  
Carbon Fibre ◽  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Fibre Composite ◽  
Guided Wave ◽  
Carbon Fibre Composite ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Ultrasonic Guided Wave ◽  
Carbon Fibre Composites

Carbon fibre composites have a promising application in the future of the vehicle, because of their high strength and light weight. Debonding is a major defect of the carbon fibre composite. The time-frequency analysis is fundamental to identify the defect on ultrasonic nondestructive evaluation and testing. In order to obtain the instantaneous frequency and the peak time of modes of the ultrasonic guided wave, an algorithm based on the Smoothed Pseudo Wigner-Ville distribution and the peak-track algorithm is presented. In the algorithm, a masking step is proposed, which can guarantee that the peak-track algorithm can automatically exact the instantaneous frequency and the instantaneous amplitude of different modes on the Smoothed Pseudo Wigner-Ville distribution. An experiment for detecting the debonding for a type of carbon fibre composite is done. The presented algorithm is employed on the experimental signals. The processed result of experimental signals reveals that the defect can stimulate new modes, and there is a quantitative relationship between the defect size and the frequency of the new mode. The presented technique provides a valuable way to detect the presentence, calculate the size, and locate the position of the debonding defect.

PCA and ICA Based Prognostic Health Monitoring of Electronic Assemblies Subjected to Simultaneous Temperature-Vibration Loads

2017 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Principal Component ◽  
Component Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Strain Components ◽  
Frequency Components ◽  
Electronic Assemblies

This paper focusses on health monitoring of electronic assemblies under vibration load of 14 G until failure at an ambient temperature of 55 degree Celsius. Strain measurements of the electronic assemblies were measured using the voltage outputs from the strain gauges which are fixed at different locations on the Printed Circuit Board (PCB). Various analysis was conducted on the strain signals include Time-frequency analysis (TFA), Joint Time-Frequency analysis (JTFA) and Statistical techniques like Principal component analysis (PCA), Independent component analysis (ICA) to monitor the health of the packages during the experiment. Frequency analysis techniques were used to get a detailed understanding of the different frequency components before and after the failure of the electronic assemblies. Different filtering algorithms and frequency quantization techniques gave insight about the change in the frequency components with the time of vibration and the energy content of the strain signals was also studied using the joint time-frequency analysis. It is seen that as the vibration time increases the occurrence of new high-frequency components increases and further the amplitude of the high-frequency components also has increased compared to the before failure condition. Statistical techniques such as PCA and ICA were primarily used to reduce the dimensions of the larger data sets and provide a pattern without losing the different characteristics of the strain signals during the course of vibration of electronic assemblies till failure. This helps to represent the complete behavior of the electronic assemblies and to understand the change in the behavior of the strain components till failure. The principal components which were calculated using PCA discretely separated the before failure and after failure strain components and this behavior were also seen by the independent components which were calculated using the Independent Component Analysis (ICA). To quantify the prognostics and hence the health of the electronic assemblies, different statistical prediction algorithms were applied to the coefficients of principal and independent components calculated from PCA and ICA analysis. The instantaneous frequency of the strain signals was calculated and PCA and ICA analysis on the instantaneous frequency matrix was done. The variance of the principal components of instantaneous frequency showed an increasing trend during the initial hours of vibration and after attaining a maximum value it then has a decreasing trend till before failure. During the failure of components, the variance of the principal component decreased further and attained a lowest value. This behavior of the instantaneous frequency over the period of vibration is used as a health monitoring feature.

Time–Frequency Analysis of Seismic Data Using a Three Parameters S Transform

2018 ◽  
Vol 15 (1) ◽  
pp. 142-146 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Bo Zhang ◽  
Fangyu Li ◽  
Qian Wang
Keyword(s):  
Frequency Analysis ◽  
Seismic Data ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform
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