Multitrace Sparse-spike Deconvolution Regularized by Spatial Derivatives

Sparse deconvolution methods frequently invert for subsurface reflection impulses and adopt a trace-by-trace processing pattern. However, following this approach causes unreliability of the estimated reflectivity due to the nonuniqueness of the inverse problem, the poor spatial continuity of structures in the reconstructed reflectivity section, and the suppression on the reflection signals with small amplitudes. We have developed a structurally constrained multichannel band-controlled deconvolution (SC-MBCD) algorithm to alleviate these three issues. The algorithm inverts for a high-resolution seismogram rather than the full-band reflectivity series, thereby reducing the multiple solutions in the inversion and enhancing the reliability of processing results. We also exploited a structural constraint term to guarantee the spatial continuity of the structures, and we enhanced the relatively weak signals. The reflection structure characteristics, defined and extracted from the observed stacked seismic data, are the core of the structural regularization item. We solved the cost function of the SC-MBCD by the alternating direction method of multipliers algorithm. Synthetic model and field data examples demonstrate the rationality of SC-MBCD and confirmed that the algorithm can provide a better inversion result than the conventional sparse spike inversion in terms of retrieving weak reflection events and guaranteeing stratal continuities.

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Impedance reconstruction using a data-driven structural regularization term

Zhou

et al. 2018

SEG Technical Program Expanded Abstracts 2018

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Multitrace Sparse-spike Deconvolution Regularized by Spatial Derivatives

Cited by 4 publications

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Band-controlled sparse deconvolution

Band-controlled sparse deconvolution

Multichannel band-controlled deconvolution based on a data-driven structural regularization

Impedance reconstruction using a data-driven structural regularization term

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