Preconditioning seismic data with 5D interpolation for computing geometric attributes
Bibliographic record
Abstract
The most common preconditioning of seismic data improves the signal-to-noise (S/N) ratio of the seismic data by removing spatial noise or enhancing the coherency and alignment of the reflection events, without unnecessary smoothing or smearing of the discontinuities. Although we usually think of removing unwanted features, we can also improve the S/N by predicting unmeasured signal, such as dead traces and lower-fold areas corresponding to unrecorded offsets and azimuths in the gathers. Missing offsets and azimuths almost always negatively impact prestack inversion and AVAz analysis. While missing offsets and azimuths may not result in sufficiently reduced S/N of stacked data to impair conventional time-structure interpretation, they usually give rise to attribute artifacts. If the inconsistencies in fold follow a regular pattern, we refer to the corresponding attribute pattern as “acquisition footprint.” Acquisition footprint is an undesirable artifact that masks the geologic features or amplitude variations seen on time slices from the seismic data, especially at shallow times. We begin our article by correlating missing data and areas of low fold to artifacts seen in seismic attributes. We then show how 5D interpolation of missing data prior to prestack migration results in more complete gathers resulting in a better balanced stack and the reduction of footprint and other attribute artifacts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".