Cross-well seismic waveform tomography for monitoring CO2 injection: a case study from the Ketzin Site, Germany
Bibliographic record
Abstract
Geological storage of CO2 is one means of mitigating the effects of continued burning of fossil fuels for power generation. An important component in the storage concept is the monitoring of the CO2 distribution at depth. Seismic methods can play a significant role in this monitoring, in particular cross-well methods are of interest due to their high resolution. For these purposes, a series of cross-well seismic surveys were acquired within the framework of the CO2SINK project at Ketzin, Germany, at various stages of an injection test. We study here the potential of applying cross-well seismic waveform tomography to monitor the CO2 injection process. First, we test the method on synthetic data having a similar geometry to that of the real data. After successful application on the synthetic data, we test the method on the real data acquired at the Ketzin Site. Traveltime tomography images of the real data show no observable differences between the surveys. However, seismic waveform tomography difference images show significant differences. A number of these differences are artefacts that can probably be attributed to inconsistent receiver coupling between the different surveys. However, near the injection horizon, below the caprock, a velocity decrease is present that is consistent with that expected from the injection process.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".