Deconvolution, Polarization and Wavelet Transform of Seismic Signals

1997 ◽  
pp. 41-66
Author(s):  
A. J. Mendecki
Keyword(s):  
Wavelet Transform ◽  
Seismic Signals

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. 

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

Using the wavelet transform to detect singularities of the thin bed seismic signals

2000 ◽  
Author(s):  
Guangming Zhu ◽  
Qingchun Li ◽  
Jian Zhu
Keyword(s):  
Wavelet Transform ◽  
Seismic Signals

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%.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

Applications of the synchrosqueezing transform in seismic time-frequency analysis

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. V55-V64 ◽  
Author(s):  
Roberto H. Herrera ◽  
Jiajun Han ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Frequency Resolution ◽  
Seismic Signals ◽  
Time Frequency ◽  
Trade Offs ◽  
Mode Decomposition ◽  
Frequency Representation ◽  
Synchrosqueezing Transform ◽  
Instantaneous Frequencies

Time-frequency representation of seismic signals provides a source of information that is usually hidden in the Fourier spectrum. The short-time Fourier transform and the wavelet transform are the principal approaches to simultaneously decompose a signal into time and frequency components. Known limitations, such as trade-offs between time and frequency resolution, may be overcome by alternative techniques that extract instantaneous modal components. Empirical mode decomposition aims to decompose a signal into components that are well separated in the time-frequency plane allowing the reconstruction of these components. On the other hand, a recently proposed method called the “synchrosqueezing transform” (SST) is an extension of the wavelet transform incorporating elements of empirical mode decomposition and frequency reassignment techniques. This new tool produces a well-defined time-frequency representation allowing the identification of instantaneous frequencies in seismic signals to highlight individual components. We introduce the SST with applications for seismic signals and produced promising results on synthetic and field data examples.

scholarly journals Dynamic Influence of Near-Source Seismic Ground Motion on Underground Mine Working Stability Using Time-Frequency Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Adam Lurka ◽  
Grzegorz Mutke ◽  
Piotr Małkowski
Keyword(s):  
Wavelet Transform ◽  
Underground Mining ◽  
Continuous Wavelet ◽  
Seismic Signals ◽  
Time Frequency ◽  
Frequency Group ◽  
Duration Time ◽  
Underground Mine Working ◽  
Frequency Properties ◽  
Strong Ground

Peak particle velocity parameter is very useful in assessing underground mine working stability. Its application is widespread and requires additional analysis of the dominant frequency of the seismic signal. In order to properly analyze the velocity amplitudes of strong ground motions generated from seismic sources, time-frequency properties of near-source seismic signals in underground mines should be quantified. Using numerical calculations, the continuous wavelet transform (CWT) of the recorded near-source seismic signals in three perpendicular directions was obtained to characterize its time-frequency properties. The properties of recorded strong ground motion velocity seismograms for two high energy seismic events and two blasts from two underground coal mines in Poland have been extracted with the use of continuous wavelet transform spectrograms showing the duration time of each frequency group. Assuming a constant peak particle velocity amplitude on the analyzed seismograms, the duration time of each frequency group starts to play a key role. The longer the duration time of the lower frequency group is on the CWT spectrogram, the more the damaging effect on underground mining excavations can be observed. Varying bandwidths of dominant frequencies in separate time intervals for the analyzed seismic signals have had significantly different influence on the potentially damaging effect on underground mining excavations.

Localization and de-noising seismic signals on SASW measurement by wavelet transform

2013 ◽  
Vol 98 ◽  
pp. 124-133 ◽  
Author(s):  
Alireza Golestani ◽  
S.Mahdi S.Kolbadi ◽  
Ali Akbar Heshmati
Keyword(s):  
Wavelet Transform ◽  
Seismic Signals
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