A theoretical and physical modeling analysis of the coupling between baseline elastic properties and time-lapse changes in determining difference amplitude variation with offset
Bibliographic record
Abstract
ABSTRACT Perturbation theory has been widely used in many applications in seismology, more recently for time-lapse problems. We have formulated a scheme for modeling linear and nonlinear elastic time-lapse difference amplitude variation with offset data. We have expressed this framework as an expansion in orders of the baseline interface properties and time-lapse changes from the time of the baseline survey to the time of the monitor survey. We have examined our formulation with the numerical data used in literature for real time-lapse data. The results indicated to the first order that our framework for time-lapse difference data is in agreement with Landrø’s linear approximation. The higher order terms represented corrections appropriate for large P- and S-wave velocities and density contrasts in the reservoir from the time of the baseline survey to the time of the monitor survey. A physical modeling data set was acquired simulating a time-lapse problem to validate our theoretical results. Plexiglas, polyvinyl chloride (PVC), and phenolic slabs were used as proxy materials to simulate the cap rock and reservoir at the time of the baseline and monitor surveys, respectively. Reflected amplitudes were picked at Plexiglas-PVC and Plexiglas-phenolic interfaces and were corrected for geometric spreading, emergence angle, free surface, transmission loss, and radiation patterns. Our results indicated that higher order expansion terms, involving products of elastic time-lapse perturbation and baseline medium perturbation, matched laboratory data with significantly reduced error in comparison with linearized forms. We have concluded that in many plausible time-lapse scenarios, the increase in accuracy associated with higher order corrections that we observed enhanced the time-lapse modeling.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".