Comparative analysis of matching pursuit algorithms for Kirchhoff migration on compressed data

  • Carlos A. Fajardo Universidad Industrial de Santander
  • Fabián Sánchez Universidad Industrial de Santander
  • Ana B. Ramirez Universidad Industrial de Santander
Keywords: Migration over compressed data, Matching Pursuit, Memory wall, Kirchhoff migration Migración sobre datos comprimidos, Migración Kirchhoff


Currently, the amount of recorded data in a seismic survey is in the order of hundreds of Terabytes. The processing of such amount of data implies significant computational challenges. One of them is the I/O bottleneck between the main memory and the node memory. This bottleneck results from the fact that the disk memory access speed is thousands-fold slower than the processing speed of the co-processors (eg. GPUs). We propose a special Kirchhoff migration that develops the migration process over compressed data. The seismic data is compressed by using three well-known Matching Pursuit algorithms. Our approach seeks to reduce the number of memory accesses to the disk required by the Kirchhoff operator and to add more mathematical operations to the traditional Kirchhoff migration. Thus, we change slow operations (memory access) for fast operations (math operations). Experimental results show that the proposed method preserves, to a large extent, the seismic attributes of the image for a compression ratio up to 20:1.


Wu, C., T. Wang, H. Wang, H. Li, and S. Liu, (2018). Full 3D double square root migration method for VTI media and its application in real data, Int. Geophys. Conf. Beijing, China, 24-27 April 2018, 600-603.

Gregg, C. and K. Hazelwood, (2011). Where is the data? Why you cannot debate CPU vs. GPU performance without the answer, ISPASS 2011 - IEEE Int. Symp. Perform. Anal. Syst. Softw., 134-144.

Salamanca, W. A., A.-B. Ramirez, and F.-A. Vivas, (2018). Comparative analysis of 3D RTM Implementation Strategies for an Efficient Use of Memory in a Single GPU, CT&F-Ciencia, Tecnol. y Futur., 8(2), 75-82.

Fajardo, C. A., Ó. M. Reyes, and J. (Universidad R. J. C. Castillo, (2018). Reducing the I/O Bottleneck by a Compression Strategy, Eng. Lett., 26(2), 203-209. [Online]. Available:

Bouska, J. and S. Gray, (1998). Migration of unequally sampled compressed seismic data, Expand. Abstr. Tech. Program, SEG 68th Annu. Meet., 1128-1130.

Yu, Z., G. A. McMechan, P. D. Anno, and J. F. Ferguson, (2004). Wavelet-transform-based prestack multiscale Kirchhoff migration, Geophysics, 69(6), 1505-1512.

Zheludev, V. A., E. Ragoza, and D. D. Kosloff, (2002). Fast Kirchhoff migration in the wavelet domain, Explor. Geophys., 33(1), 23-27.

Alkhalifah, T., (2011). Efficient traveltime compression for 3D prestack Kirchhoff migration, Geophys. Prospect., 59(1), 1-9.

Mallat, S. G., (1993). Matching pursuits with time-frequency dictionaries, IEEE Trans. Signal Process., 41(12), 3397-3415.

Wang, B. and K. Pann, (1996). Kirchhoff migration of seismic data compressed by matching pursuit decomposition, in Expanded abstracts of the technical program, SEG 66th annual meeting, (1), 1642--1645.

Li, X.--G., B. Wang, K. Pann, J. Anderson, and L. Deng, (1998). Fast migration using a matching pursuit algorithm, in Expanded Abstracts of the Technical Program, SEG 68th Annual Meeting, 1732-1735.

Lin, L., B. Shi, and P. An, (2016). Multiwavelet prestack Kirchhoff migration, Geophysics, 81(3), S79-S85.

Xiongwen, W., W. Huazhong, and Z. Xiaopeng, (2014). High dimensional seismic data interpolation with weighted matching pursuit based on compressed sensing, J. Geophys. Eng., 11(6).

Wang, Y., (2007). Seismic time-frequency spectral decomposition by matching pursuit, Geophysics, 72(1), V13-V20.

Liu, J. and K. J. Marfurt, (2005). Matching pursuit using Morlet wavelets., in 2005 SEG Annual Meeting, (4), 786-790.

Lin, H., Y. Li, H. Ma, B. Yang, and J. Dai, (2015). Matching-pursuit-based spatial-trace time-frequency peak filtering for seismic random noise attenuation, IEEE Geosci. Remote Sens. Lett., 12(2), 394-398.

Gholamy, A. and V. Kreinovich, (2014). Why Ricker wavelets are successful in processing seismic data: Towards a theoretical explanation, IEEE SSCI 2014 - 2014 IEEE Symp. Ser. Comput. Intell. - CIES 2014 2014 IEEE Symp. Comput. Intell. Eng. Solut. Proc., 11-16.

Duval, L. C. and V. Buitran, (2001). Compression denoising: using seismic compression for uncoherent noise removal, EAGE 63rd Conf. Tech. Exhib. Netherlands, (June).

Cai, T. T. and L. Wang, (2011). Orthogonal matching pursuit for sparse signal recovery with noise, IEEE Trans. Inf. Theory, 57(7), 4680-4688.

Pati, Y. C., R. Rezaiifar, and P. S. Krishnaprasad, (1993). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, in Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on, 40-44. [Online]. Available:

Santos*, P. N. and R. C. Pestana, (2015). Least-squares Kirchhoff migration using traveltimes based on the maximum amplitude criterion by the rapid expansion method, in 14th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 3-6 August 2015, 1043-1047.

Vidale, J., (1988). Finite-difference calculation of travel times, Bull. Seismol. Soc. Am., 78(6), 2062-2076. Available at:

How to Cite
Fajardo, C. A., Sánchez, F., & Ramirez, A. B. (2021). Comparative analysis of matching pursuit algorithms for Kirchhoff migration on compressed data . CT&F - Ciencia, Tecnología Y Futuro, 11(1), 47–53.


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