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Seismic denoising using curvelet analysis

Author

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  • Oliveira, M.S.
  • Henriques, M.V.C.
  • Leite, F.E.A.
  • Corso, G.
  • Lucena, L.S.

Abstract

A curvelet is a new and effective spectral transform, that allows sparse representations of complex data. It has many applications in several fields, including denoising, wave propagation in disordered media and pattern recognition. This spectral technique is based on directional basis functions that represent objects having discontinuities along smooth curves. In this work we apply this technique to the removal of Ground Roll, which is an undesired feature signal present in seismic data obtained by sounding the geological structures of the Earth. In this methodology the original seismic data is decomposed by curvelet transform in scales and angular domains. For each scale the curvelet denoising technique allows a very efficient separation of the Ground Roll in angle sections. The precise identification of the Ground Roll pattern allows an effective erasing of its coefficients. In contrast to conventional denoising techniques we do not use any artificial attenuation factor to decrease the amplitude of the Ground Roll coefficients. We have estimated that, depending on the scale, around 75% of the energy of the strong undesired signal is removed.

Suggested Citation

  • Oliveira, M.S. & Henriques, M.V.C. & Leite, F.E.A. & Corso, G. & Lucena, L.S., 2012. "Seismic denoising using curvelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2106-2110.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:5:p:2106-2110
    DOI: 10.1016/j.physa.2011.04.009
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    References listed on IDEAS

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    1. Leite, F.E.A. & Montagne, Raúl & Corso, G. & Vasconcelos, G.L. & Lucena, L.S., 2008. "Optimal wavelet filter for suppression of coherent noise with an application to seismic data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(7), pages 1439-1445.
    2. Corso, G. & Kuhn, P.S. & Lucena, L.S. & Thomé, Z.D., 2003. "Seismic ground roll time–frequency filtering using the gaussian wavelet transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 318(3), pages 551-561.
    3. Montagne, Raúl & Vasconcelos, Giovani L., 2006. "Extremum criteria for optimal suppression of coherent noise in seismic data using the Karhunen–Loève transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(1), pages 122-125.
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    Cited by:

    1. Henriques, M.V.C. & Leite, F.E.A. & Andrade, R.F.S. & Andrade, J.S. & Lucena, L.S. & Neto, M. Lucena, 2015. "Improving the analysis of well-logs by wavelet cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 130-140.
    2. He, Kaijian & Lu, Xingjing & Zou, Yingchao & Keung Lai, Kin, 2015. "Forecasting metal prices with a curvelet based multiscale methodology," Resources Policy, Elsevier, vol. 45(C), pages 144-150.

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