1887

Abstract

Summary

Multiscale and multidirectional transforms were introduced to represent non-spiky reflectivity instead of the assumption of spiky reflectivity in the deconvolution problem. The study found that an alternative sparse Shearlet coefficient can be used to well represent the non-spiky reflectivity and solve the problem in multichannel way. Such non-spiky reflectivity can help avoid loss of weak reflection events, which is likely to occur in conventional methods due to over sparse constraints on spiky reflectivity. Moreover, compared to single-trace deconvolution methods, the multichannel method can enhance the continuity of reflection events and suppress high-frequency noise in the deconvolved data. Seismic inversion is usually considered an ill-conditioned problem, and normally requires regularization of deconvolution operators. In this study, we proposed multichannel sparse deconvolution of seismic data with Shearlet-Cauchy constrained inversion. Firstly, a stable method that enables accurate reflectivity estimation was developed based on maximum a posteriori estimation in Bayesian statistics.Then sparse Shearlet coefficients are used to represent non-spiky refelectivity. According to the different distributions of noise and signal in Shearlet domain, thresholding methods can be used to suppress noise and increase the noise resistance of proposed method. A comparison of synthetic data with field seismic data demonstrated the validity of the proposed method.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201700724
2017-06-12
2024-03-29
Loading full text...

Full text loading...

References

  1. ClaerboutJ. F., MuirF.
    , 1973. Robust modeling with erratic data [J]. Geophysics, 38(5): 826–844.
    [Google Scholar]
  2. KumarV, HerrmannF J.
    , 2008. Deconvolution with curvelet-domain sparsity[C]// Seg Technical Program Expanded Abstracts. 1996–2000.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201700724
Loading
/content/papers/10.3997/2214-4609.201700724
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