Seismic Data compression using 2D Lifting-Wavelet algorithms

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Carlos Augusto Fajardo Ariza https://orcid.org/0000-0002-8995-4585
Oscar Mauricio Reyes Torres https://orcid.org/0000-0003-3947-9197
Ana Beatriz Ramirez Silva

Keywords

seismic data compression, wavelet transform, lifting

Abstract

Different seismic data compression algorithms have been developed in order to make the storage more efficient, and to reduce both the transmission time and cost. In general, those algorithms have three stages: transformation, quantization and coding. The Wavelet transform is highly used to compress seismic data, due to the capabilities of theWavelets on representing geophysical events in seismic data. We selected the lifting scheme to implement the Wavelet transform because it reduces both computational and storage resources. This work aims to determine how the transformation and the coding stages affect the data compression ratio. Several 2D lifting-based algorithms were implemented to compress three different seismic data sets. Experimental results obtained for different filter type, filter length, number of decomposition levels and coding scheme, are presented in this work.

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References

L. C. Duval and T. Q. Nguyen, "Seismic data compression: a comparative study between GenLOT and wavelet compression," Proc. SPIE, Wavelets: Appl. Signal Image Process., vol. 3813, pp. 802–810, 1999.

M. A. T, "Towards an Efficient Compression Algorithm for Seismic Data," Radio Science Conference, 2004., pp. 550–553, 2004.

W. Wu, Z. Yang, Q. Qin, and F. Hu, "Adaptive Seismic Data Compression Using Wavelet Packets," 2006 IEEE International Symposium on Geoscience and Remote Sensing, no. 3, pp. 787–789, 2006.

F. Zheng and S. Liu, "A fast compression algorithm for seismic data from non-cable seismographs," 2012 World Congress on Information and Communication Technologies, pp. 1215–1219

A. Gercho and R. Gray, Vector quantization and signal compression. Kluwer Academic Publiser, 1992.
http://dx.doi.org/10.1007/978-1-4615-3626-0

P. Aparna and S. David, "Adaptive Local Cosine transform for Seismic Image Compression," 2006 International Conference on Advanced Computing and Communications, no. x, pp. 254–257, 2006.

J. M. Lervik, T. Rosten, and T. A. Ramstad, "Subband Seismic Data Compression: Optimization and Evaluation," Digital Signal Processing Workshop Proceedings, no. 1, pp. 65–68, 1996.

A. a. Aqrawi and A. C. Elster, "Bandwidth Reduction throughMultithreaded Compression of Seismic Images," 2011

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, pp. 1730–1739, May 2011.
http://dx.doi.org/10.1109/IPDPS.2011.330

W. Xi-zhen, T. Yun-tina, G. Meng-tan, and J. Hui, "Seismic data compression based on integer wavelet transform," ACTA SEISMOLOGICA SINICA, vol. 17, no. 4, pp. 123–128, 2004.

A. A. Vassiliou and M. V. Wickerhouser, "Comparison of wavelet image coding schemes for seismic data compression," in Optical Science, Engineering and Instrumentation'97. International Society for Optics and Photonics,1997, pp. 118–126.

P. L. D. J. D. Villasenor, R. A. Ergas, "Seismic Data Compression Using High-Dimensional Wavelet Transforms," in Data Compression Conference, 1996., 1996, pp. 396–405.

X. Xie and Q. Qin, "Fast Lossless Compression of Seismic Floating-Point Data," 2009 International Forum on Information Technology and Applications, no. 40204008, pp. 235–238, May 2009.

A. Z. Averbuch, F. Meyer, J. O. Stromberg, R. Coifman, and A. Vassiliou, "Low bit-rate efficient compression for seismic data." IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 10, no. 12, pp. 1801–1814, 2001.

D. Salomon, Data Compression The Complete Reference, 4th ed. Springer, 2007.

W. Sweldens, "Lifting scheme: a new philosophy in biorthogonal wavelet constructions," in SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics and Photonics, 1995, pp. 68–79.

O. Yilmaz, Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data. Society of Exploration Geophysicists, 2008.

C. E. Shannon, "A Mathematical Theory of Communication," The Bell Sys- tem Technical Journal, vol. XXVII, no. 3, pp. 379–423, 1948.
http://dx.doi.org/10.1002/j.1538-7305.1948.tb01338.x

L. C. Duval and T. Rosten, "Filter bank decomposition of seismic data with application to compression and denoising," SEG Annual International Meeting Soc. Expl. Geophysicists, pp. 2055–2058, 2000.

K. Andra, C. Chakrabarti, T. Acharya, and S. Member, "A VLSI Architecture for Lifting-Based Forward and Inverse Wavelet Transform," Signal Processing, IEEE Transactions on, vol. 50, no. 4, pp. 966–977, 2002.
http://dx.doi.org/10.1109/78.992147

M. Jansen and P. Oonincx, Second generation wavelets and applications. Springer, 2005.

W. Sweldens, "Wavelets and the Lifting Scheme : A 5 Minute Tour," ZAMM-Zeitschrift fur Angewandte Mathematik und Mechanik, vol. 76, no. 2, pp. 41–47, 1996.

D. L. Fugal, Conceptual Wavelets in Digital Signal Processing: An In-depth, Practical Approach for the Non-mathematician. Space & Signals Technical Pub., 2009.

T. Chen, "Seismic Data compression: a tutorial," 1995.

D. Salomon, Variable-length Codes for Data compression. Springer, 2007.
http://dx.doi.org/10.1007/978-1-84628-959-0

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