The effect of interpolation on imaging and AVO: A Viking case study
Bibliographic record
Abstract
Abstract The use of prestack interpolation prior to prestack migration to improve AVO analysis on image gathers is demonstrated on an exploration play. The interpolation achieves this improvement by reducing migration artifacts. AVO analysis attempts to estimate fundamental information from surface seismic data and likely will be used more frequently if the estimates can be more accurately produced. Land 3D seismic typically has poor and irregular sampling. This poor sampling creates migration noise, which is a material cause of inaccurate AVO estimates. Prestack 5D interpolation is applied prior to prestack migration and AVO analysis on the imaged gathers to address this noise problem. The interpolation algorithm includes offset and azimuth dimensions that preserve AVO information. This method is evaluated bycomparing the results to those of alternate approaches, such as superbinning, that suppress this kind of noise in AVO analysis. The evaluation is determined by comparing our ability to predict the reservoir quality of a gas-charged sandstone reservoir with 48 well penetrations. We compare migrated gathers, AVO attribute stacks, and attribute maps in our analysis. We also generate scatter plots of the AVO attribute values against measures of reservoir quality at the well control points to allow a quantitative measure of the improvements. The interpolation method yields gathers, stacks, and maps that all appear to be better resolved and less noisy than the other methods. The scatter plots demonstrate a measurable and significant improvement from the interpolation method, especially compared to superbinning. This work suggests interpolation before imaging, and imaging before AVO analysis, should be performed on land 3D surface seismic data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".