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Multichannel Sparse Deconvolution of Seismic Data with Shearlet-Cauchy Constrained Inversion
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 79th EAGE Conference and Exhibition 2017, Jun 2017, Volume 2017, p.1 - 5
Abstract
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.