Adaptive Multitrace Seismic Deconvolution via Structural L1-2 Minimization

Deconvolution technology, as an effective means to enhance the resolution of seismic data, has emerged as a prominent research area in the field of seismic exploration. However, due to its inherent ill-posed nature, seismic deconvolution poses significant challenges. The conventional approach for deconvolution employs a sparse inversion strategy to reconstruct underground reflection coefficients but overlooks the spatial relationship between adjacent seismic traces, resulting in inadequate spatial continuity of the deconvolved results. In this letter, we propose an adaptive seismic deconvolution strategy that incorporates spatial continuity regularization based on local seismic similarity. The adaptive multitrace deconvolution process consists of three parts: first, we consider spatial continuity by introducing a spatial regularization term derived from local seismic similarity; second, we formulate an objective function by combining
$L1$
-2 norm for sparse regularization with terms accounting for misfit and spatial regularization to reconstruct reflectivity; finally, we solve the objective function using alternative direction method of multipliers (ADMMs) and L-BFGS algorithms. Our proposed method effectively preserves weak effective signals while providing a clearer depiction of geological body distribution and ensuring superior spatial continuity in complex geological structures. Synthetic and field data tests demonstrate that our proposed method yields high-resolution deconvolved results with strong spatial continuity.