From shallow to deep: Enhancing seismic resolution with weak supervision
Bibliographic record
Abstract
ABSTRACT High-resolution (HR) seismic data are crucial for accurately identifying subsurface geologic formations and reservoir properties. However, as widely observed in prestack and poststack data, seismic wave attenuation often leads to a gradual decrease in resolution with depth. This degradation leads to resolution differences between data from shallow and deep layers, characterized by high-frequency energy loss in deeper target layers. The latter could complicate the extraction of crucial deep-layer structure information, leading to increased uncertainty in reservoir modeling and significant economic losses due to our inability to image deep targets with sufficient resolution. Traditional methods with simplified assumptions about this nonlinear attenuation hinder modeling geologic complexity and variability. Deep-learning methods excel at capturing complex, nonlinear relationships but often rely heavily on scarce and costly paired labels for supervised learning. As a result, deep learning without requiring paired labels for seismic resolution enhancement has become a key research focus. This study explores a method to enhance 3D seismic data resolution using weakly supervised learning, leveraging the inherent similarity of sedimentary structures across depths. Specifically, our approach uses a relatively shallow, HR data window to transfer and extrapolate learned high-frequency information to a deeper window containing attenuated responses from deeper layers. Thereby, we achieve enhanced resolution in the target region. A tailored 3D convolutional neural network with a bidirectional cycle structure, custom-designed loss functions, and data preprocessing techniques specifically addresses the challenge of resolution differences caused by seismic wave attenuation. The effectiveness of our method is validated with synthetic and real 3D poststack migration data, demonstrating its robustness for regions near the target layer. Compared with conventional spectral whitening, our approach leverages intrinsic data characteristics more adaptively and robustly, making geologic structures more discernible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".