Enhanced intertrace variations extraction via a self-supervised network for prestack seismic analysis
Bibliographic record
Abstract
ABSTRACT Prestack seismic analysis is crucial for characterizing subsurface geology, offering valuable insights through seismic reflections across diverse offsets or azimuths. Although existing methods have achieved notable success, they tend to primarily focus on specific intertrace differences or directly equate lateral intertrace variations with vertical waveform characteristics. This limits their potential to capture subtle nuances within geologic bodies. In this paper, we develop an innovative self-supervised neural network that enhances the extraction of comprehensive intertrace variations, enabling the more effective characterization of geologic details. Our network simulates the analytical capabilities of geologists, interpreting individual traces through their contextual relationships within the gather. We develop a unique feature mask reconstruction layer that masks the features of each trace, leveraging information from the surrounding traces for feature reconstruction. The network then reconstructs the original gather by combining these regenerated features, ensuring the completeness of the information. By focusing on feature-level analysis, the network diverges from basic data interpolation strategies, promoting a thorough extraction of intrinsic intertrace relationships. Field data experiments determine that our method surpasses conventional approaches in capturing prestack intertrace variations, facilitating more precise hydrocarbon reservoir descriptions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".