CycleGAN integration of high-resolution crooked lines into 3D seismic volumes: Enhancing data set resolution on the Loess Plateau, China
Bibliographic record
Abstract
ABSTRACT 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".