Reconstructing seismic data using the accelerated linearized Bregman method and iterative soft thresholding algorithm

Abstract

Seismic data reconstruction plays a crucial role in seismic exploration and imaging, facilitating accurate subsurface characterization and geological interpretation. In this study, we propose a novel approach for reconstructing seismic data using the accelerated linearized Bregman method (ALBM) in conjunction with the iterative soft thresholding algorithm (ISTA). This combined method, referred to as ALBM+ISTA, aims to exploit the complementary strengths of ALBM’s accelerated convergence properties and ISTA’s sparsity-promoting capabilities. The proposed approach is evaluated through theoretical simulations and case studies, wherein specific quantitative metrics such as signal-to-noise ratio (SNR), structural similarity index, root mean square error, peak signal-to-noise ratio, and edge preservation index are employed to assess reconstruction quality and accuracy. Results demonstrate that ALBM+ISTA offers improved performance in terms of noise suppression, structural fidelity, and edge preservation compared to existing reconstruction techniques. Furthermore, the method exhibits adaptability to diverse seismic acquisition geometries and noise levels, making it a promising tool for enhancing seismic data processing in various geological science applications.