Nonstationary differential resolution: An algorithm to improve seismic resolution

Seismic deconvolution used for improving the bandwidth of data is inherently nonstationary, mixed phase, and blind. Due to some restricting assumptions imposed by conventional deconvolution methods, they are either stationary or semiblind. A fully nonstationary blind deconvolution method is proposed that is able to simultaneously take into account different sources of nonstationarity and to improve the bandwidth of highly nonstationary seismic data in a fully blind manner. Based on the concept of block convolution and the overlap method, the convolutional model of seismic data is generalized to consider nonstationary cases and to model nonstationary data. This generalized convolutional model is then used for nonstationary blind deconvolution, in which the statistical characteristics of the wavelets are allowed to arbitrarily change in the vertical and horizontal directions. Given a nonstationary seismic record, several time-space-varying wavelets are simultaneously determined with the reflectivity model in an alternating direction algorithm using a variational approach. Numerical tests are presented showing the high performance of our nonstationary blind deconvolution for improving the temporal resolution of data in comparison with their stationary counterparts. The results indicate that in comparison with patched deconvolution, our nonstationary method is more robust and stable for different window sizes and it produces better results with a higher signal-to-noise ratio.

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Blind sparse-spike deconvolution with thin layers and structure

Sui

2020

GEOPHYSICS

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Blind sparse-spike deconvolution is a widely used method to estimate seismic wavelets and sparse reflectivity in the shape of spikes based on the convolution model. To increase the vertical resolution and lateral continuity of the estimated reflectivity, we further improve the sparse-spike deconvolution by introducing the atomic norm minimization and structural regularization which, respectively, improve the vertical resolution and lateral continuity of the estimated reflectivity. Specifically, we use the atomic norm minimization to estimate the reflector locations ,which are further used as position constraints in the sparse-spike deconvolution. By doing this, we can vertically separate highly thin layers through the sparse deconvolution. In addition, the seismic structural orientations are estimated from the seismic image to construct a structure-guided regularization in the deconvolution to preserve the lateral continuity of reflectivities. Our improvements are suitable for most types of sparse-spike deconvolution approaches. The sparse-spike deconvolution method with a Toeplitz-sparse matrix factorization (TSMF) is used as an example to demonstrate the effectiveness of our improvements. Synthetic and real examples show that our methods perform better than TSMF in estimating reflectivity of thin layers and preserving lateral continuities.

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Nonstationary differential resolution: An algorithm to improve seismic resolution

Cited by 4 publications

References 14 publications

Enhancing Seismic Resolution via Hyperbolic Spaces S Transform

Enhancing Seismic Resolution via Hyperbolic Spaces S Transform

Nonstationary blind deconvolution of seismic records

Blind sparse-spike deconvolution with thin layers and structure

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