Singularity detection of the thin bed seismic signals with wavelet transform

2000 ◽  
Vol 13 (1) ◽  
pp. 61-66
Author(s):  
Qing-Chun Li ◽  
Guang-Ming Zhu
Keyword(s):  
Wavelet Transform ◽  
Seismic Signals ◽  
Singularity Detection

Singularity detection in FOG-based pavement data by wavelet transform

2017 ◽  
Author(s):  
Dandan Yang ◽  
Lixin Wang ◽  
Wenbin Hu ◽  
Zhen Zhang ◽  
Jinghua Fu ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Singularity Detection

scholarly journals Applicataion of ormsby wavelet for generation of synthetic seismic signals

2018 ◽  
Vol 7 (2.7) ◽  
pp. 794
Author(s):  
E Sai Sumanth ◽  
V Joseph ◽  
Dr K S Ramesh ◽  
Dr S Koteswara Rao
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Transforms ◽  
Seismic Signal ◽  
Core Structure ◽  
Seismic Signals ◽  
Seismic Reflections ◽  
Input Wave

Investigation of signals reflected from earth’s surface and its crust helps in understanding its core structure. Wavelet transforms is one of the sophisticated tools for analyzing the seismic reflections. In the present work a synthetic seismic signal contaminated with noise is synthesized  and analyzed using Ormsby wavelet[1]. The wavelet transform has efficiently extracted the spectra of the synthetic seismic signal as it smoothens the noise present in the data and upgrades the flag quality of the seismic data due to termers. Ormsby wavelet gives the most redefined spectrum of the input wave so it could be used for the analysis of the seismic reflections. 

scholarly journals Signal Analysis On Soil Stress From Vibrating Compaction Based On Wavelet Transform

2014 ◽  
Vol 60 (2) ◽  
pp. 257-268
Author(s):  
Zhang Qing-Zhe ◽  
Yan Bing ◽  
Dai Jing-Liang ◽  
Yang Bao-Gui
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Frequency Component ◽  
Start Time ◽  
Transform Method ◽  
Singularity Detection ◽  
Study Results ◽  
Soil Stress ◽  
Stress Signal ◽  
Stress Signals

Abstract The paper presented the wavelet transform method for d e-noising and singularity detection to soil compressive stress signal. The study results show that the reconstruction signals by the wavelet de-noising keeps the low frequency component at [0, 31.25Hz] of the original signal and improves the high frequency property at other frequency bands. The impaction time from the start time to resonance time of the stress signals is varies with the depth of the soil. With the increase of times of compaction, the impaction time of the stress is decreasing in every layer. But the speed of reaching compacted status in each layer is different.

scholarly journals Signature Matching For Seismic Signal Identification

2021 ◽  
Author(s):  
Pankaj Jadhav ◽  
Debabrata Datta ◽  
Siddhartha Mukhopadhyay
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
Wavelet Coherence ◽  
Continuous Wavelet ◽  
Seismic Signals ◽  
Time Frequency ◽  
Maximum Covariance Analysis ◽  
Cross Wavelet Transform ◽  
Matching Techniques

Seismic signals can be classified as natural or manmade by matching signature of similar events that have occurred in the past. Waveform matching techniques can be effectively used for discrimination since the events with similar location and focal mechanism have similar waveform irrespective of magnitude. The seismic signals are inherently non-stationary in nature. The analysis of such signals can be best achieved in multiresolution framework by resolving the signal using continuous wavelet transform (CWT) in time-frequency plane. In this paper similarity testing and classification of nuclear explosion and earthquake are exploited with correlation, continuous wavelet transform, cross-wavelet transform and wavelet coherence (WC) of P phase of seismogram. Clustering of seismic signals continuous wavelet spectra is performed using maximum covariance analysis. The proposed classifier has an average classification accuracy of 94%.

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