Estimating Injection-Induced Vp Changes using Cross-Well Seismic Full-Waveform Inversion at the Aquistore CO2 Storage Site, Canada
Bibliographic record
Abstract
Summary Carbon capture and storage (CCS) is accepted as an important means of reducing carbon dioxide (CO2) release into the atmosphere. Different time-lapse seismic imaging methods with various resolutions can track the extent of the CO2 plume. Full-waveform inversion (FWI) of seismic data provides high-resolution seismic velocity models. Although time-lapse full-waveform inversion (TL-FWI) has shown potential for monitoring the subsurface, it is more complex than classic FWI. This is because the time-lapse changes are usually limited in terms of extent and magnitude. There are also different sources of non-repeatability (NR) between the baseline (BL) and monitor acquisitions. In this study, we apply TL-FWI to crosswell seismic data acquired at the Aquistore site in Saskatchewan, Canada, after the injection of more than 390,000 tonnes of CO2. The objective of this study is to analyze the efficiency of TL-FWI for detecting velocity variations due to the injected CO2. While there are different sources of NR in this study, the employed technique can identify the injection zones. This shows the potential of FWI for estimating the velocity changes at this site.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".