scholarly journals NOISE ATTENUATION IN SEISMIC REFLECTION DATA COMBINING EMPIRICAL MODE DECOMPOSITION AND SVD FILTERING

2016 ◽  
Vol 34 (3) ◽  
Author(s):  
Felipe Da Mota Alves ◽  
Milton J. Porsani
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Reflection ◽  
Seismic Noise ◽  
Singular Value ◽  
Noise Attenuation ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Mode Decomposition ◽  
General Frequency ◽  
Value Decomposition

ABSTRACT. Noises are common events in seismic reflection that have very striking features in the seismograms, hindering the data processing and interpretation. The attenuation of seismic noise is a challenge, in general frequency filters are employed, but they often do not show good results. The characteristic of noise...Keywords: seismic noise, Empirical Mode Decomposition, Singular Value Decomposition. RESUMO. Ruídos são eventos comuns na sísmica de reflexão que possuem características bem marcantes nos sismogramas, atrapalhando o processamento e interpretação dos dados. A atenuação de ruídos sísmicos é um desafio, em geral são utilizados filtros de...Palavras-chave: ruídos sísmicos, Decomposição em Modos Empíricos, Decomposição em Valores Singulares.

Denoising Lidar Signal Based on Ensemble Empirical Mode Decomposition and Singular Value Decomposition

2017 ◽  
Vol 46 (12) ◽  
pp. 1201003
Author(s):  
程知 CHENG Zhi ◽  
何枫 HE Feng ◽  
靖旭 JING Xu ◽  
张巳龙 ZHANG Si-long ◽  
侯再红 HOU Zai-hong
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Lidar Signal ◽  
Mode Decomposition ◽  
Value Decomposition

Random and coherent noise attenuation by empirical mode decomposition

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. V89-V98 ◽  
Author(s):  
Maïza Bekara ◽  
Mirko van der Baan
Keyword(s):  
Empirical Mode Decomposition ◽  
User Interaction ◽  
Data Sets ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Constant Frequency ◽  
Mode Decomposition ◽  
Reduction Methods ◽  
Low Performance ◽  
Value Decomposition

We have devised a new filtering technique for random and coherent noise attenuation in seismic data by applying empirical mode decomposition (EMD) on constant-frequency slices in the frequency-offset [Formula: see text] domain and removing the first intrinsic mode function. The motivation behind this development is to overcome the potential low performance of [Formula: see text] deconvolution for signal-to-noise enhancement when processing highly complex geologic sections, data acquired using irregular trace spacing, and/or data contaminated with steeply dipping coherent noise. The resulting [Formula: see text] EMD method is equivalent to an autoadaptive [Formula: see text] filter with a frequency-dependent, high-wavenumber cut filtering property. Removing both random and steeply dipping coherent noise in either prestack or stacked/migrated sections is useful and compares well with other noise-reduction methods, such as [Formula: see text] deconvolution, median filtering, and local singular value decomposition. In its simplest implementation, [Formula: see text] EMD is parameter-free and can be applied to entire data sets without user interaction.

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

2017 ◽  
Vol 232 (12) ◽  
pp. 2280-2296 ◽  
Author(s):  
Shuiguang Tong ◽  
Yidong Zhang ◽  
Jian Xu ◽  
Feiyun Cong
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Fault Classification ◽  
Mode Decomposition ◽  
Value Decomposition

In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification.

Performance Comparison of Dense Frequency Identifications for Empirical Mode Decomposition

2011 ◽  
Vol 378-379 ◽  
pp. 266-269
Author(s):  
Min Zheng ◽  
Fan Shen
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Wavelet Packet ◽  
Performance Comparison ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Value Decomposition

Empirical Mode Decomposition(EMD) suffers some difficulties in separating dense frequencies. The Wavelet Packet Transform (WPT) and Singular-Value Decomposition (SVD) as signal preprocessors were used to decompose a simulated signal with dense frequency components and the performances of two signal preprocess technologies were compared in this paper. The results show that Singular-Value Decomposition (SVD) as preprocessor was better in separating dense frequencies than Wavelet Packet Transform (WPT).

scholarly journals Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. H29-H37 ◽  
Author(s):  
Bradley Matthew Battista ◽  
Camelia Knapp ◽  
Tom McGee ◽  
Vaughn Goebel
Keyword(s):  
Signal Processing ◽  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
The Hilbert Transform

Advancements in signal processing may allow for improved imaging and analysis of complex geologic targets found in seismic reflection data. A recent contribution to signal processing is the empirical mode decomposition (EMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT). The EMD empirically reduces a time series to several subsignals, each of which is input to the same time-frequency environment via the Hilbert transform. The HHT allows for signals describing stochastic or astochastic processes to be analyzed using instantaneous attributes in the time-frequency domain. The HHT is applied herein to seismic reflection data to: (1) assess the ability of the EMD and HHT to quantify meaningful geologic information in the time and time-frequency domains, and (2) use instantaneous attributes to develop superior filters for improving the signal-to-noise ratio. The objective of this work is to determine whether the HHT allows for empirically-derived characteristics to be used in filter design and application, resulting in better filter performance and enhanced signal-to-noise ratio. Two data sets are used to show successful application of the EMD and HHT to seismic reflection data processing. Nonlinear cable strum is removed from one data set while the other is used to show how the HHT compares to and outperforms Fourier-based processing under certain conditions.

scholarly journals Estimating broad trend of acoustic impedance profile from observed seismic reflection data using first principles only

2020 ◽  
Vol 17 (3) ◽  
pp. 475-483
Author(s):  
Animesh Mandal ◽  
Santi Kumar Ghosh
Keyword(s):  
First Principles ◽  
Acoustic Impedance ◽  
Seismic Reflection ◽  
Low Frequency ◽  
Singular Value ◽  
Earth Model ◽  
Depth Range ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Impedance Profile

Abstract Estimation of broad features or the low-frequency part of acoustic impedance from conventional reflection data is an essential yet challenging step for quantitative interpretation of seismic data due to its band-limited nature. A missing low-frequency part leads to non-uniqueness in the solution as well as placing restrictions in recovering the absolute impedance values. The current industry practice fills this gap by assuming either an initial impedance model or statistical restrictions on such a model. Doing away with such assumptions but using only first principles (Zoeppritz's equations) and homogeneous layered earth model, we have formulated a set of linear equations that are then solved for an unknown reflection co-efficient using singular value decomposition (SVD) approach with time sampled seismic trace as the input data. The present work demonstrates the effectiveness of reconstructing a broad and smooth impedance profile from first principles and even from acquired seismic reflection data. It also illustrates the method's success with real data, while determining in one go the unknown scale factor linking the true and the relative seismic amplitudes, and the smallest singular value to be retained in the solution from only the knowledge of the average value of the acoustic impedance over the depth range in question. Thus, the salient feature of this work is the ability to reconstruct an approximate impedance profile from field data without the aid of an initial model or statistical assumption on the reflectivity series. This approximate impedance profile can serve as a reliable initial input for more refined inversion or geologic interpretation.

A Feature Extraction Method of Gear Fault Based on the SVD EMD and Morphology

2013 ◽  
Vol 433-435 ◽  
pp. 477-482 ◽  
Author(s):  
Gao Yan Hou ◽  
Yong Lv ◽  
Hao Huang ◽  
Yi Zhu
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Extraction Method ◽  
Singular Value ◽  
Background Signal ◽  
Feature Extraction Method ◽  
Filtering Method ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Value Decomposition

In order to extract the weak signal from strong background signal characteristics, a feature extraction method combined of the singular value decomposition (SVD), empirical mode decomposition (EMD) and mathematical morphology was proposed. The signal got through the singular value decomposition first. Next took the average value of the decomposed main components. And carried on the empirical mode decomposition and selected the main component to summate and refactor. Then morphological difference filter was used to extract the frequency characteristics of the fault signal. The results of numerical simulation test and gear fault simulation experiments show that the proposed method can clearly extract the frequency characteristics of weak signal from strong background signal and noise. Comparison has been done with the results of singular value decomposition (SVD) and morphological filtering method and empirical mode decomposition form of filtering method. It proves the effectiveness of the proposed method.

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