Frequency Domain Seismic Blind Deconvolution Based on ICA with High Order Statistics Constraint

Wei Gao; Huai Shan Liu; Jian Ye Sun

doi:10.4028/www.scientific.net/AMM.321-324.1827

Paper Titles

Study of the Lumped Parameter Models and Parameters Identification of Hydraulic Bushing
p.1805

Study on Optimal Design of Flywheel Structure Based on APDL
p.1812

Study on the Method to Create the 3D Arterial Trees of Human Tongue
p.1817

Surface Reconstruction via Interpolating Boundary Curves and Cross-Boundary Derivatives Simultaneously Approximating Inner Points
p.1821

Frequency Domain Seismic Blind Deconvolution Based on ICA with High Order Statistics Constraint
p.1827

A Technology of CNC Punching of Chinese Characters
p.1831

Based on Road Green Wave Effect of Collaborative Strategy of Signal Timing Fuzzy Control
p.1836

Analysis on the Method of Length Calculating for Flexible Hoisting Extra Large Solder Ball Grid Roof
p.1842

The Design and Application of DCS in Control System for Ceramic Kiln
p.1848

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Frequency Domain Seismic Blind Deconvolution Based...

Frequency Domain Seismic Blind Deconvolution Based on ICA with High Order Statistics Constraint

Abstract:

Independent components analysis (ICA) with constraint of seismic wavelet estimated from bispectrum of seismic traces is combined with short time Fourier transforms (STFT) to improve the traditional frequency domain seismic deconvolution. Neglecting noise, the seismic record is changed from time domain to frequency domain with STFT in order to transform the common seismic model to the basic ICA model. By applying FastICA algorithm with constraint of seismic wavelet estimated from bispectrum of seismic traces, reflectivity series and the seismic wavelet can be produced in frequency domain and changed back to the time domain subsequently. The model and real seismic data numerical examples all show the algorithm valid.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

1827-1830

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.1827

Citation:

Cite this paper

Online since:

June 2013

Authors:

Wei Gao, Huai Shan Liu, Jian Ye Sun

Keywords:

Bispectrum, Frequency Domain Deconvolution, Independent Component Analysis (ICA), Short Time Fourier Transform (STFT)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Bell A J, Sejnowski T J. An Information-Maximization Approach to Blind Separation and Blind Deconvolution [J]. Neural Computation (S0899-7667), 1995, 7(6): 1129-1159.

DOI: 10.1162/neco.1995.7.6.1129

Google Scholar

[2] Park H M, Dhir C S, Oh S H, at el. A Filter Fank Approach to Independent Component Analysis for Convolved Mixtures [J]. Neurocomputing (S0925-2312), 2006, 69(16-18): 2065-2077.

DOI: 10.1016/j.neucom.2005.09.016

Google Scholar

[3] Kaplan, S. T., Ulrych, T. J., (2003) "Blind Deconvolution and ICA with a Banded Mixing Matrix", Online Proc. of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, April 2003, Nara, Japan, pp.591-596.

Google Scholar

[4] LIU Xiwu, GAO Wei, ZHANG Ning, LIU Wanying. ICA with Banded Mixing Matrix Based Seismic Blind Deconvolution[J]. PROGRESS IN GEOPHYSICS, 2007, 22(4): 1153-1163.

Google Scholar

[5] Gao Wei, Liu Huaishan, Zhang Jin. ICA with Homomorphic Deconvolution Based Seismic Blind Deconvolution. Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on, 2009, 2: 421-424.

DOI: 10.1109/ifcsta.2009.225

Google Scholar

[6] Liu Xiwu, Liu Hong, Survey on seismic blind deconvolution, Progress In Geophysics, vol.18, No.18, pp.203-209, (2003)

Google Scholar

[7] Molgedey L, Schuster H G. Separation of a Mixture of Independent Signals using Time Delayed Correlations [J]. Physical Review Letters (S1079-7114), 1994, 72(23): 3634-3637.

DOI: 10.1103/physrevlett.72.3634

Google Scholar