scholarly journals A simple method inspired by empirical mode decomposition for denoising seismic data

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. V403-V413 ◽  
Author(s):  
Julián L. Gómez ◽  
Danilo R. Velis
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Computational Cost ◽  
Classical Formula ◽  
Coherent Noise ◽  
Simple Method ◽  
Mode Decomposition ◽  
The Mean ◽  
User Friendly ◽  
User Intervention

We developed a new and simple method for denoising seismic data, which was inspired by data-driven empirical mode decomposition (EMD) algorithms. The method, which can be applied either as a trace-by-trace process or in the [Formula: see text] domain, replaces the use of the cubic interpolation scheme, which is required to calculate the mean envelopes of the signal and the residues, by window averaging. The resulting strategy is not viewed as an EMD per se, but a user-friendly version of EMD-based algorithms that permits us to attain, in a fraction of the time, the same level of noise cancellation as standard EMD implementations. Furthermore, the proposed method requires less user intervention and easily processes millions of traces in minutes rather than in hours as required by conventional EMD-based techniques on a standard PC. We compared the performance of the new method against standard EMD methods in terms of computational cost and signal preservation and applied them to denoise synthetic and field (microseismic and poststack) data containing random, erratic, and coherent noise. The corresponding [Formula: see text] EMDs implementations for lateral continuity enhancement were analyzed and compared against the classical [Formula: see text] deconvolution to test the method.

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.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples

2019 ◽  
Vol 73 (12) ◽  
pp. 1436-1450 ◽  
Author(s):  
Fabiola León-Bejarano ◽  
Martin O. Méndez ◽  
Miguel G. Ramírez-Elías ◽  
Alfonso Alba
Keyword(s):  
Empirical Mode Decomposition ◽  
Raman Spectra ◽  
Biological Samples ◽  
Biological Material ◽  
Noise Levels ◽  
Synthetic Material ◽  
High Noise ◽  
Mode Decomposition ◽  
Mean Filter ◽  
The Mean

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA ([Formula: see text]) outperforms VRA ([Formula: see text]) for moderate to high noise levels whereas mVRA outperformed EMD ([Formula: see text]) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results ([Formula: see text]. For Raman spectra of synthetic material, good performance of the three methods ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

scholarly journals Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71374-71386 ◽  
Author(s):  
Wen-Long Hou ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang ◽  
Meng-Di Deng ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Random Noise ◽  
Shearlet Transform ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

scholarly journals Application of variational mode decomposition to seismic random noise reduction

2017 ◽  
Vol 14 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Zhiming Wang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Computational Cost ◽  
Superior Performance ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Reconstruction Performance ◽  
Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

Over-Sifting Components Analysis in Bidimensional Empirical Mode Decomposition

2010 ◽  
Vol 159 ◽  
pp. 377-382
Author(s):  
Guang Tao Ge
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Mean ◽  
Components Analysis ◽  
Bidimensional Empirical Mode Decomposition

Define the course of getting mean envelope as an operation (mean envelope operation) in Empirical mode decomposition (EMD), so as to express the Intrinsic Mode Function (IMF) with mean envelopes. Summarize several rules of the mean envelope operation. On this fundamental, the abnormal components exist in the over-sifting IMFs are extracted out, and the conclusion is testified with the infinite sifting experiment.

B-SPLINE ANALYTICAL REPRESENTATION OF THE MEAN ENVELOPE FOR EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 08 (02) ◽  
pp. 175-195 ◽  
Author(s):  
TIANXIANG ZHENG ◽  
LIHUA YANG
Keyword(s):  
Empirical Mode Decomposition ◽  
Basic Concept ◽  
Spline Functions ◽  
Mathematical Foundation ◽  
Knot Insertion ◽  
B Spline ◽  
Explicit Formulation ◽  
Novel Approach ◽  
Mode Decomposition ◽  
The Mean

This paper investigates how the mean envelope, the subtrahend in the sifting procedure for the Empirical Mode Decomposition (EMD) algorithm, represents as an expansion in terms of basis. To this end, a novel approach that gives an alternative analytical expression using B-spline functions is presented. The basic concept lies mainly on the idea that B-spline functions form a basis for the space of splines and have refined-node representations by knot insertion. This newly-developed expression is essentially equivalent to the conventional one, but gives a more explicit formulation on this issue. For the purpose of establishing the mathematical foundation of the EMD methodology, this study may afford a favorable opportunity in this direction.

Empirical Mode Decomposition and Quantization Effects

2015 ◽  
Vol 18 ◽  
pp. 175-183
Author(s):  
Michal Bejček ◽  
Josef Kokeš
Keyword(s):  
Empirical Mode Decomposition ◽  
Quantization Noise ◽  
Simple Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition

The article deals with phenomena that arise when trying to apply EMD decomposition of signals with quantization noise. It explains the basic procedures of EMD as a part of Hilbert-Huang transform and shows how it can be affected by quantization. A simple method to suppress these phenomena is proposed and examples to illustrate the functionality of this method are shown.

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