1887

Abstract

Summary

According to the acoustic wave theory, the amplitude and phase variations of seismic wave are usually dominated by the coupling of the structures-associated reflectivity and the fluids-associated intrinsic attenuation. However, either the intrinsic attenuation or reflectivity interference is ignored when improving seismic resolution by using the conventional methods. To solve the issue, we introduce a weighted L1 norm to evaluate the relationship between the sparsity of the inverted reflectivity and the tested Q. Through measuring the sparsity of the inverted reflectivity with different tested Q, we proposed semi-blind time-variant sparse deconvolution using a weighted L1 norm to separate Q and reflectivity, and to further obtain the high-resolution result. Furthermore, the weighted L1 norm can enhance the contributions of weak signals and meanwhile suppress the contributions of strong signals by using different weights inversely proportional to amplitude, which can reduce the dependence of our method on the reflectivity model. Finally, the proposed method is tested on statistically synthetic, physical modeling and field data.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901363
2019-06-03
2020-06-02
Loading full text...

Full text loading...

References

  1. Aghamiry, H. S., and Gholami, A.
    [2018] Interval-Q estimation and compensation: an adaptive dictionary-learning approach: Geophysics, 83, no. 4, V233–V242.
    [Google Scholar]
  2. Bickel, S. H., and Natarajan, R. R.
    [1985] Plane-wave Q deconvolution: Geophysics, 50, no.9, 1426–1439.
    [Google Scholar]
  3. Ma, M., Wang, S. X., Yuan, S. Y., Gao, J. H., and Li, S. J.
    [2018] Multichannel block sparse Bayesian learning reflectivity inversion with lp-norm criterion-based Q estimation: Journal of Applied Geophysics, 159, 434–445.
    [Google Scholar]
  4. YuanS. Y., WangS. X., MaM., JiY. Z., and DengL.
    [2017] Sparse Bayesian learning-based time-variant deconvolution: IEEE Transactions on Geoscience and Remote Sensing, 55, no 11, 6182–6194.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901363
Loading
/content/papers/10.3997/2214-4609.201901363
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