High-Resolution Reconstruction of Seismic Data with Cycle-Consistent Adversarial Network

High-resolution seismic reconstruction is a challenging inverse problem because field seismic traces are inherently band-limited and their high-frequency components are further degraded by source bandwidth limitations, acquisition conditions, random noise, and attenuation during wave propagation. Classical resolution enhancement methods can partially sharpen seismic events, but they usually rely on restrictive assumptions about stationarity, minimum-phase wavelets, or accurate attenuation models. In this study, we propose a structure-preserving bidirectional bandwidth translation network for seismic resolution enhancement. Instead of formulating the task as a one-way paired regression problem, the proposed approach interprets resolution enhancement as unpaired translation between low-bandwidth and high-bandwidth seismic domains. A cycle-consistent adversarial objective is combined with an SSIM-based structural constraint so that the model simultaneously improves spectral recovery, waveform fidelity, and reflector continuity. To reduce the domain gap between synthetic and field data, we further construct a hybrid training corpus by combining field-extracted wavelets with synthetic reflectivity sequences and train a lightweight one-dimensional residual generator–discriminator architecture tailored to oscillatory seismic traces. Comprehensive experiments are conducted on synthetic data, a field seismic profile, and the public SEG Open Data benchmark. In addition to comparisons with conventional deconvolution and time-varying frequency deconvolution, the manuscript reports quantitative comparisons with representative learning-based baselines, together with ablation studies, parameter sensitivity analysis, robustness evaluation under different noise levels and bandwidth settings, and computational cost analysis. The results show that the proposed method consistently achieves a favorable balance between spectral extension and structural preservation, demonstrating its potential as a practical data-driven solution for seismic resolution enhancement.