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Multichannel Deconvolution Based on Spatial Structure Regularization
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 81st EAGE Conference and Exhibition 2019, Jun 2019, Volume 2019, p.1 - 5
Abstract
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 deconvolution (SCMD) 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 are the core of the structural regularization item. We solved the cost function of the SCMD by the alternating direction method of multipliers algorithm. Synthetic model and field data examples demonstrate the rationality of SCMD 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.