Combined elastic FWI of accelerometer and DAS VSP data from a CO2 sequestration test site in Newell County, Alberta
Bibliographic record
Abstract
Seismic monitoring is a key facilitator for monitoring in car- bon dioxide (CO2) sequestration projects. Distributed acoustic sensing (DAS) data is expected to be a key contributor for realizing this goal. The data supplied by DAS fibers can, in principal, be used in full waveform inversion (FWI) to supply high resolution images of subsurface properties to monitor CO2 plume growth. In this paper we apply elastic FWI to invert a VSP dataset acquired prior to CO2 injection with DAS fiber and collocated accelerometers through simultaneous inclusion of both datasets in one objective function. Observations of the similarity in inverted models for various mixtures of DAS and accelerometer data, and the agreement between field and simulated data suggests a level of robustness in our inverted baseline models of the field site. To improve the converge of FWI, we discuss two methods to overcome the complexity of field data inversion. The first inverts for an effective source field that addresses complex near surface wavefield propagation and incomplete source signature information. The second is a special data driven parameterization that prevents cross-talk in the inverted models.
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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 | 0.000 | 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".