3-D Full-Waveform Inversion of the “Snowflake” Baseline Dataset: Toward Monitoring of CO₂ Storage Through Inversion of Multioffset, Multiazimuth VSP Data at the Newell County Facility in Alberta, Canada
Bibliographic record
Abstract
The carbon capture and storage (CCS) project is gaining attention for its role in greenhouse gas reduction. In the CCS project, monitoring injected CO2 is crucial for safe and sustainable operation. The Containment and Monitoring Institute (CaMI) project has been launched to secure CO2 monitoring techniques, particularly using the time-lapse seismic survey. In this work, we apply 3-D acoustic full-waveform inversion (FWI) to the walk-away and walk-around vertical seismic profiling data. To construct a baseline P-wave velocity model for future monitoring studies, we compare the performance of 2-D and 3-D FWI on this data. We first conduct a synthetic FWI test using a 1-D velocity model created from well-log data to identify optimal parameters and potential issues. Finally, we apply FWI to real data and analyze the inverted results. As a result, compared with 2-D FWI, we verify that 3-D FWI can be a valuable tool to build a baseline model, anticipating its future extension into 4-D seismic monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".