EMD-seislet transform

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. A27-A32 ◽  
Author(s):  
Yangkang Chen ◽  
Sergey Fomel
Keyword(s):  
Data Processing ◽  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Seismic Events ◽  
Variable Frequency ◽  
Seismic Data Processing ◽  
Local Slope ◽  
Mode Decomposition ◽  
Frequency Components

The seislet transform uses a prediction operator that is connected to the local slope or frequency of seismic events. We have combined the 1D nonstationary seislet transform with empirical-mode decomposition (EMD) in the [Formula: see text]-[Formula: see text] domain. We used EMD to decompose data into smoothly variable frequency components for the following 1D seislet transform. The resultant representation showed remarkable sparsity. We developed a detailed algorithm and used a field example to demonstrate the application of the new seislet transform for sparsity-promoting seismic data processing.

Automatic data extrapolation to zero offset along local slope

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. U1-U12
Author(s):  
Michelângelo G. Silva ◽  
Milton J. Porsani ◽  
Bjorn Ursin
Keyword(s):  
Data Processing ◽  
Least Squares ◽  
Local Time ◽  
Seismic Data ◽  
Total Least Squares ◽  
Velocity Analysis ◽  
Seismic Data Processing ◽  
Local Slope ◽  
Zero Offset ◽  
Data Extrapolation

Velocity-independent seismic data processing requires information about the local slope in the data. From estimates of local time and space derivatives of the data, a total least-squares algorithm gives an estimate of the local slope at each data point. Total least squares minimizes the orthogonal distance from the data points (the local time and space derivatives) to the fitted straight line defining the local slope. This gives a more consistent estimate of the local slope than standard least squares because it takes into account uncertainty in the temporal and spatial derivatives. The total least-squares slope estimate is the same as the one obtained from using the structure tensor with a rectangular window function. The estimate of the local slope field is used to extrapolate all traces in a seismic gather to the smallest recorded offset without using velocity information. Extrapolation to zero offset is done using a hyperbolic traveltime function in which slope information replaces the knowledge of the normal moveout (NMO) velocity. The new data processing method requires no velocity analysis and there is little stretch effect. All major reflections and diffractions that are present at zero offset will be reproduced in the output zero-offset section. Therefore, if multiple reflections are undesired in the output, they should be removed before data extrapolation to zero offset. The automatic method is sensitive to noise, so for poor signal-to-noise ratios, standard NMO velocities for primary reflections can be used to compute the slope field. Synthetic and field data examples indicate that compared with standard seismic data processing (velocity analysis, mute, NMO correction, and stack), our method provides an improved zero-offset section in complex data areas.

Deconvolutive Radon transform

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V117-V125 ◽  
Author(s):  
Ali Gholami
Keyword(s):  
Data Processing ◽  
Temporal Resolution ◽  
Radon Transform ◽  
Seismic Data ◽  
Model Building ◽  
Random Noise ◽  
Velocity Model ◽  
Seismic Events ◽  
Seismic Data Processing ◽  
Band Limited

The Radon transform (RT) plays an important role in seismic data processing for its ability to focus seismic events in the transform domain. The band-limited nature of seismic events due to the blurring effects of the source wavelet, however, causes a decrease in the temporal resolution of the transform. We have developed the deconvolutive RT (DecRT) as a generalization of conventional RT and to increase the temporal resolution. Unlike the conventional counterpart, the new basis functions can take an arbitrary shape in the time direction. This method is thus proposed to adaptively infer the temporal wave shape from the input data while finding a sparse representation of it. The new transform significantly improves the sparsity and thus the temporal resolution of the resulting seismic data. The applicability of the hyperbolic DecRT in seismic data processing is demonstrated for random noise attenuation, primary and multiple separation, high-quality stacking, and automatic velocity model building. The results obtained on synthetic and field data sets confirm the effectiveness of the method in improving the time and slowness/curvature resolutions compared with conventional transforms, which leads to improved seismic processing results in the deconvolutive Radon domains.

APPLICATION OF THE EMPIRICAL MODE DECOMPOSITION METHOD TO GROUND-ROLL NOISE ATTENUATION IN SEISMIC DATA

2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Low Frequency ◽  
High Amplitude ◽  
Seismic Line ◽  
Seismic Processing ◽  
Mode Decomposition ◽  
Ground Roll ◽  
Fortran 90 ◽  
Emd Method

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

Innovative Seismic Data Processing Approach to Improve Signal to Noise Ratio

2001 ◽  
Author(s):  
P.P. Gupta ◽  
Kuldeep Prakash ◽  
Paramjeet Singh ◽  
M.N. Lakra
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Seismic Data Processing ◽  
Signal To Noise ◽  
Processing Approach ◽  
Noise Ratio
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