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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".