Bibliographic record
Abstract
ABSTRACT The objective of most seismic time-lapse studies is to detect rock-property changes in a subsurface formation caused by fluid withdrawal or injection, often by comparing seismic reflection images of the subsurface before and after the operation. Because rock-property changes can affect the amplitudes of seismic reflection events associated with the boundaries of the formation, amplitude anomalies are the usual target of time-lapse experiments. Sometimes, however, particularly in harder, less porous rocks, a seismic amplitude anomaly can be relatively small and difficult to detect. There is a secondary time-lapse effect, however, which may be detectable even in the absence of a significant reflectivity anomaly: the time delay of reflections from layers beneath a formation whose wave propagation velocity has been altered by the pore-fluid change. We have developed a near-surface correction technique for land data, which we call joint raypath interferometry, to specifically enhance and detect small time delays between corresponding events on two or more comparable time-lapse seismic images. We demonstrate the technique first on a numerical model, and then on an actual time-lapse field survey in which a reflection amplitude anomaly is difficult to detect.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".