scholarly journals The continuous wavelet transform in processing data of high frequency electromagnetic prospecting

2016 ◽  
Vol 19 (2) ◽  
pp. 81-93
Author(s):  
Tin Quoc Chanh Duong ◽  
Dau Hieu Duong ◽  
Van Thanh Nguyen ◽  
Thuan Van Nguyen
Keyword(s):  
Wavelet Transform ◽  
Data Processing ◽  
Continuous Wavelet Transform ◽  
High Frequency ◽  
Theoretical Models ◽  
Vital Role ◽  
Continuous Wavelet ◽  
Ground Penetrating ◽  
Non Destructive ◽  
Shallow Structure

Ground Penetrating Radar (GPR), a high frequency electromagnetic prospecting method (10 to 3000 MHz) has been rapidly developed in recent decades. With many advantages such as non-destructive, fast data collection, high precision and resolution, this method is a useful means to detect underground targets. It is currently used in the research of studying the shallow structure for examples: forecast landslide, subsidence, mapping urban underground works, traffic, construction, archaeology and other various fields of engineering, GPR data processing is becoming increasingly urgent. Wavelet transform is one of the new signal analysis tools, plays a vital role in numerous domains like image processing, graphics, data compression, gravitational, electromagnetic and geomagnetic data processing, GPR and some others. In this study, we used the continuous wavelet transform (CWT) and multiscale edge detection (MED) with the wavelet functions which were appropriately selected to determine underground targets. The accuracy of this technique was tested on some theoretical models before being applied on experimental data. The obtained results showed that this was a feasible method that could be used to detect the size and position of the anomaly objects.

scholarly journals Processing seismic ambient noise data with the continuous wavelet transform to obtain reliable empirical Green's functions

2020 ◽  
Vol 222 (2) ◽  
pp. 1224-1235
Author(s):  
Yang Yang ◽  
Chunyu Liu ◽  
Charles A Langston
Keyword(s):  
Wavelet Transform ◽  
Data Processing ◽  
Continuous Wavelet Transform ◽  
Ambient Noise ◽  
Continuous Wavelet ◽  
Denoising Method ◽  
Transient Signals ◽  
Empirical Green’S Functions ◽  
Noise Data ◽  
Correlated Signals

SUMMARY Obtaining reliable empirical Green's functions (EGFs) from ambient noise by seismic interferometry requires homogeneously distributed noise sources. However, it is difficult to attain this condition since ambient noise data usually contain highly correlated signals from earthquakes or other transient sources from human activities. Removing these transient signals is one of the most essential steps in the whole data processing flow to obtain EGFs. We propose to use a denoising method based on the continuous wavelet transform to achieve this goal. The noise level is estimated in the wavelet domain for each scale by determining the 99 per cent confidence level of the empirical probability density function of the noise wavelet coefficients. The correlated signals are then removed by an efficient soft thresholding method. The same denoising algorithm is also applied to remove the noise in the final stacked cross-correlogram. A complete data processing workflow is provided with the overall data processing procedure divided into four stages: (1) single station data preparation, (2) removal of earthquakes and other transient signals in the seismic record, (3) spectrum whitening, cross-correlation and temporal stacking and (4) remove the noise in the stacked cross-correlogram to deliver the final EGF. The whole process is automated to make it accessible for large data sets. Synthetic data constructed with a recorded earthquake and recorded ambient noise is used to test the denoising method. We then apply the new processing workflow to data recorded by the USArray Transportable Array stations near the New Madrid Seismic Zone where many seismic events and transient signals are observed. We compare the EGFs calculated from our workflow with commonly used time domain normalization method and our results show improved signal-to-noise ratios. The new workflow can deliver reliable EGFs for further studies.

Development of Spike Wavelet Analysis and Its Application to Damage Analysis on Gearbox

2006 ◽  
Vol 321-323 ◽  
pp. 1233-1236
Author(s):  
Sang Kwon Lee ◽  
Jang Sun Sim
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Gear System ◽  
Frequency Resolution ◽  
Linear Scaling ◽  
Continuous Wavelet ◽  
Vibration Signals ◽  
Gear Fault

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.

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