e-book Covariance Analysis for Seismic Signal Processing

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Signal Process. Benoliel, S. Frequency wavenumber approach of the s-p transform: some applications in seismic data processing. Geophysics 35 5 , Embree, P. Wide band velocity filtering—the pie-slice process. Geophysics 28 6 , Foster, D.

Covariance-Based Wavefield Separation and its Application in Crosswell Seismic Data

Suppression of multiple reflections using the radon transform. Geophysics 57 3 , Hanna, M. Velocity filters for multiple interference attenuation in geophysical array data. IEEE Trans. Remote Sens. Natasha Hendrick. Multi-component seismic wavefield separation via spectral matrix filtering. ASEG extended abstractth Geophysical conference. Melbourne, Australia, Kirlin, R. Data covariance matrices in seismic signal processing. Can SEGRec 26 4 , Le Bihan, N. Singular value decomposition of quaternion matrices: a new tool for vector-sensor signal processing.

Ling, G. A blind source separation method applied to simultaneous kinetic multicomponent determination. Materials , Liu, B. Covariance-based wavefield separation and its application in crosswell seismic data. Mari, J. Spectral matrix filtering applied to VSP processing. Revuede l'Institut Francais du Petrole 45 3 , Paulus, Caroline, Mars, Jerome I. New multicomponent filters for geophysical data processing. Paulus, C. Wideband spectral matrix filtering for multicomponent sensor array.

Pillai, S. Forward backward spatial smoothing techniques for coherent signal identification.


  • Spatial coherence of the seismic wavefield continuously recorded by the USArray.
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  • Covariance analysis for seismic signal processing!

Speech Signal Process. Rao, B. Weighted subspace methods and spatial smoothing: analysis and comparison. Rutty, M. Wavefield decomposition using spectral matrix techniques. Vrabie, V. Modified singular value decomposition by means of independent component analysis. Zhou, Binzhong, Greenhalgh, Stewart, Multiple suppression by 2D filtering in the parabolic s-p domain: a wave-equation-based method.

Geophysics 44 3 , Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix Academic research paper on " Computer and information sciences ".

Basic Geophysics: Processing IV: Migration

Acoustic zoom high-resolution seismic beamforming for imaging specular and non-specular energy of deep oil and gas bearing geological formations. Dlaye a Dept. Introduction In seismic exploration, a wavelet is sent to the earth layers and the reflected seismic wavefields, due to the impedance mismatches between different geological layers, are recorded by linear arrays of multi-component sensors Al-Qaisi et al. Peer review under responsibility of King Saud University. Elsevier Production and hosting by Elsevier of the seismic wavefield data Al-Qaisi et al.

This long data vector has been created by reorganizing the frequency transform of recorded multicomponent seismic wavefield data into one column vector. The proposed mathematical model for multicomponent seismic wavefield In this section, the proposed mathematical model for multi-component seismic wavefield is presented. Figure 2 Multi-component wavefield seismic data set.

The long data vector yf that is shown in Fig.

Spatial coherence of the seismic wavefield continuously recorded by the USArray

Long Data Vector Frequency Bins Figure 3 The real part of the long data vector y f that contains all the frequency bins on all sensors for each component. The propagation characteristic term up, f Kf , that describes the multicom-ponent array response of the received waves. The column vector Xp f expressed as in Eq. The direction of arrival of the seismic source 0p. The attenuation factors ap, Pp. The emitted seismic wavelet wp f. The proposed blind seismic wavefield separation algorithm In this section, the proposed blind separation algorithm will be mathematically derived.

The estimated spectral covariance matrix can be mathematically written as in Eq. However, it is computationally expensive to decompose the spectral matrix Eyy1 f. Hence, the complexity is reduced by a factor greater H-Component 5 10 15 20 Figure 5 Reduced dimension covariance spectral matrix.

Number of eigenvalues Figure 7 Eigenvalues of estimated reduced dimension covariance spectral matrix. The Eyy1 f matrix is expressed in Eq.

Results and analysis In this section, many experiments have been conducted on a noisy synthetic and real multi-component wave-field seismic data to examine the effectiveness of the proposed approach. Figure 11 Evaluation of MSE versus projected eigenvector for the second separated seismic wave. Add to basket. Add to wishlist. What is wishlist?

Covariance-Based Wavefield Separation and its Application in Crosswell Seismic Data

Engineering in general Geophysics Seismology, volcanology. Short description: Rather than address one seismic data-processing problem and present several methods, this book presents one fundamental methodology - analysis of the sample covariance matrix - and many seismic data problems to which it applies, providing the geophysical signal analyst with sufficient material to understand the usefulness of this approach. Long description: Rather than address one seismic data-processing problem and present several methods, this book presents one fundamental methodology - analysis of the sample covariance matrix - and many seismic data problems to which it applies, providing the geophysical signal analyst with sufficient material to understand the usefulness of this approach.

Table of Contents: Rather than address one seismic data-processing problem and present several methods, this book presents one fundamental methodology - analysis of the sample covariance matrix - and many seismic data problems to which it applies, providing the geophysical signal analyst with sufficient material to understand the usefulness of this approach. Relevant matches. Recently viewed. Related to your recently viewed titles.

Subject Lookup. Covariance analysis for seismic signal processing. Author Lyou, Chin Mei.

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Date of Issue School School of Electrical and Electronic Engineering. Abstract The early investigation of ground target seismic disturbances detection were conventionally implemented with arrays of multiple vertical-component geophones. It is possible, though, to compute a bearing estimate of a ground surface disturbance using a single tri-axial or three-component geophone.