Reconstruction and Compensation of Missing Trace and Attenuation Seismic Data: Integrating Nonlinear Activation-Free Network With Attention Mechanisms

Attenuation and the occurrence of missing traces are prevalent challenges in field seismic data, significantly affecting subsequent processing and imaging. While numerous studies have been conducted to address these issues, both traditional methods and deep learning approaches often treat seismic data reconstruction and attenuation compensation as distinct problems. This fragmented approach may neglect the potential correlation and shared characteristics between compensation and reconstruction. Consequently, this paper presents a novel nonlinear activation-free network (CSNAF-Net3+) specifically designed to simultaneously conduct seismic data reconstruction and attenuation compensation. The proposed network integrates U-Net3+, NAFNet, and CBAM methodologies, leveraging the automatic feature learning and end-to-end processing capabilities inherent in deep learning. This integration significantly enhances performance in both reconstruction and compensation tasks. Numerical simulation and field data tests demonstrate that CSNAF-Net3+ exhibits exceptional efficacy in addressing issues related to missing traces, attenuation, and noise. Notably, it achieves substantial improvements in reconstructing and compensating for extensive gaps within continuously missing traces. Moreover, we further introduce a more accurate and robust compensation method based on a multi-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> value model training framework. This strategy possesses significant potential to improve the network’s adaptability for field seismic data.