1887

Abstract

Summary

Seismic inversion methods are highly sensitive to the noise present in the data set. The need to enhance the signal-to-noise ratio (SNR) motivates the researchers do develop increasingly sophisticated denoising methods and combine them into other techniques. While some methodologies operate on a single scale, the curvelet transform established itself as multi-scale transform useful to decompose the seismic signals into multi-resolution elements. In this study, we evaluate the benefits of curvelet denoising as a preconditioning method to poststack seismic data in an 2D acoustic inversion processing using a Bayesian framework. Our tests on a synthetic data set modelled from the Marmousi model and the real data set from the Brazilian offshore Campos Basin have shown that the curvelet thresholding method can be successfully applied for random noise elimination. Even the use of a hard global threshold might allow improvements in the deepest parts. Future work will have to show whether alternatives that ensure a more robust way of selecting the coefficients can take into account the wavelength change with depth variation.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201700582
2017-06-12
2020-03-30
Loading full text...

Full text loading...

References

  1. Candès, E.J., Demanet, L., Donoho, D. and Ying, L.
    [2006] Fast discrete curvelet transforms. Multiscale Modeling & Simulation, 5(3), 861–899.
    [Google Scholar]
  2. Candès, E.J. and Donoho, D.L.
    [2000] Curvelets: A surprisingly effective nonadaptive representation for objects with edges. Tech. Rep. No. 28, Stanford University.
    [Google Scholar]
  3. Ma, J. and Plonka, G.
    [2010] The Curvelet Transform. IEEE Signal Processing Magazine, 27(2), 118–133.
    [Google Scholar]
  4. Mallat, S.
    [1999] A Wavelet Tour of Signal Processing, Second Edition (Wavelet Analysis & Its Applications). Academic Press.
    [Google Scholar]
  5. Starck, J.L., Candès, E.J. and Donoho, D.L.
    [2002] The curvelet transform for image denoising. IEEE Transactions on image processing, 11(6), 670–684.
    [Google Scholar]
  6. Ulrych, T.J. and Sacchi, M.D.
    [2005] Information-based inversion and processing with applications, 36. Elsevier.
    [Google Scholar]
  7. Versteeg, R.
    [1994] The Marmousi experience: Velocity model determination on a synthetic complex data set. The Leading Edge, 13(9), 927–936.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201700582
Loading
/content/papers/10.3997/2214-4609.201700582
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error