Separation of Reflection from Diffraction Events via the CRS Technique and a Blind Source Separation Method based on Sparsity Maximization

Duarte, Leonardo Tomazeli; Lopes, Renato da Rocha; Faccipieri, Jorge H.; Romano, João Marcos Travassos; Tygel, Martin

doi:10.1190/sbgf2013-296

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…It is worth noticing the presence of a large amount of temporal samples (in the case of signals) or pixels (in the case of images) that take values that are almost null. Examples of sparse signals and images arise in different domains, including biomedical signal processing [33], geophysics [34], and audio processing [35].…”

Section: A Sparse Signalsmentioning

confidence: 99%

Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals

Silva

Duarte

Attux

2016

JCIS

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The problem of blind source separation (BSS) has been intensively studied by the signal processing community. The first solutions to deal with BSS were proposed in the 1980's and are founded on the concept of independent component analysis (ICA). More recently, aiming at tackling some limitations of ICAbased methods, much attention has been paid to alternative BSS approaches. In this tutorial, in addition to providing a brief review of the classical BSS framework, we present two research trends in this area, namely source separation over Galois fields and sparse component analysis. For both subjects, we provide an overview of the main criteria, highlighting scenarios that can benefit from these more recent BSS paradigms.

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Section: A Sparse Signalsmentioning

confidence: 99%

Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals

Silva

Duarte

Attux

2016

JCIS

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“…Obviously, the noise amplification depends on the measurement function, the method of reconstruction, and the dynamical system as proved in paper [40]. It means that the x A (1) x A (4) x A (5) x Figure 3: Singular values of the measured signal processed by the different 4th-order cumulants slices. method of SCBSS for nonlinear time series is not robust for the different SNR and the different factors such as or/and .…”

Section: Hos-based Coordinatementioning

confidence: 99%

“…As one of the most attractive solutions for the blind source separation (BSS) problem, independent component analysis (ICA) has a strong practical background and wide applications in multiway data analysis such as biomedicine [1], image processing [2], telecommunications [3], geophysical research field [4,5], and physics of musical instruments [6,7], because it is a combination of informationism, optimal theory, probability, matrix theory, and mathematical statistics. Indeed, several papers recently have been written in which standard linear ICA, for instantaneous mixtures, was successfully applied to many natural environments as explosion-quakes and tremor at Stromboli and Erebus volcanoes [8,9], to study acoustical and mechanical vibrational field in organ pipe [10].…”

Section: Introductionmentioning

confidence: 99%

A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics

Xiao

Wei

et al. 2015

Mathematical Problems in Engineering

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Methods of utilizing independent component analysis (ICA) give little guidance about practical considerations for separating single-channel real-world data, in which most of them are nonlinear, nonstationary, and even chaotic in many fields. To solve this problem, a three-step method is provided in this paper. In the first step, the measured signal which is assumed to be piecewise higher order stationary time series is introduced and divided into a series of higher order stationary segments by applying a modified segmentation algorithm. Then the state space is reconstructed and the single-channel signal is transformed into a pseudo multiple input multiple output (MIMO) mode using a method of nonlinear analysis based on the high order statistics (HOS). In the last step, ICA is performed on the pseudo MIMO data to decompose the single channel recording into its underlying independent components (ICs) and the interested ICs are then extracted. Finally, the effectiveness and excellence of the higher order single-channel ICA (SCICA) method are validated with measured data throughout experiments. Also, the proposed method in this paper is proved to be more robust under different SNR and/or embedding dimension via explicit formulae and simulations.

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“…(2011). Another important application of the CDS operator was to enhance diffraction events and to separate them from reflection events (Garabito et al ., 2003; Duarte et al ., 2013; Rad et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Prestack seismic data interpolation and enhancement with common‐reflection‐surface–based migration and demigration

Garabito

2021

Geophysical Prospecting

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The standard common‐reflection‐surface stacking method simulates high‐quality zero‐offset stacked data from multi‐coverage prestack data and, as by‐products, provides three kinematic wavefield attributes in two dimensions that can be applied to solve reflection seismic problems. One of the most significant applications of those attributes is for interpolation and enhancement of prestack data through the partial common‐reflection‐surface stack approach. Because this interpolation method is based on the stacking process, it usually introduces spurious noises that can create artefacts in the migration results. In order to overcome these limitations, a new prestack data interpolation and enhancement method is presented by applying the attributes and the common‐reflection‐surface operator based on two fundamental seismic imaging operations: the Kirchhoff‐type time migration and demigration. The diffraction curves required to apply these two fundamental operations are particular cases of the common‐reflection‐surface stack operator, which better fits the diffraction events and, consequently, is the best approximation for the Kirchhoff migration operator. From synthetic and real data, impulse responses of the migration and demigration operations are shown to demonstrate how this new approach regularizes and interpolates prestack data. A simple synthetic example shows good accuracy for reconstructed reflection events and well‐resolved conflicting dip events. The application example in real data reveals that the proposed approach provides cleaner regularized sections with better reconstructed events than the results of the well‐known partial common‐reflection‐surface stack approach. It has been successfully shown that the proposed approach is a well‐founded alternative for interpolation and denoising of seismic data and produces better preconditioned data for prestack migration.

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Separation of Reflection from Diffraction Events via the CRS Technique and a Blind Source Separation Method based on Sparsity Maximization

Cited by 6 publications

References 7 publications

Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals

Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals

A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics

Prestack seismic data interpolation and enhancement with common‐reflection‐surface–based migration and demigration

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