CycleGAN integration of high-resolution crooked lines into 3D seismic volumes: Enhancing data set resolution on the Loess Plateau, China

The Loess Plateau in China presents a formidable challenge for seismic exploration due to its thick, porous surface loess layers that severely attenuate high-frequency seismic waves, degrading the resolution of conventional 3D acquisition. However, the region's unique topography, crisscrossed by deep gullies formed through consistent rainfall erosion, provides a natural solution to acquire high-resolution (HR) data. With thin or absent loess cover, these gullies delineate natural pathways ideal for 2D crooked-line seismic surveys, where reduced loess interference preserves high-frequency content. Accordingly, these 2D surveys deliver better resolution than traditional 3D acquisition in the loess-covered areas. Their seismic response distributions are expected to closely resemble those of a hypothetical HR 3D data set unaffected by loess attenuation. Although these localized 2D surveys capture geologically representative HR features, existing methods struggle to extrapolate their high-frequency characteristics to broader 3D volumes, limiting their ability to mitigate loess-induced resolution loss. To bridge this gap, we use a cycle-generative adversarial network under weak supervision to enhance 3D data resolution by leveraging unpaired 2D HR crooked-line data. Specifically, our approach transfers high-frequency features from 2D profiles to 3D volumes processed by conventional swath techniques through a bidirectional cycle structure, enforcing cross-distribution consistency while preserving geologic integrity. Custom loss functions and data augmentation further address spectral mismatches and stabilize training under loess-induced complexity. Synthetic and field experiments demonstrate that our method effectively captures HR characteristics of 2D data and recovers high-frequency content attenuated by loess in 3D data. Our approach achieves improved fidelity and noise robustness compared with traditional spectral whitening and zero-phase spiking deconvolution. This work underscores the untapped potential of integrating spatially sparse but information-rich 2D surveys with modern deep-learning methods to overcome persistent resolution limitations in seismic exploration.