Time-lapse attenuation variations using distributed acoustic sensing vertical seismic profile data during CO2 injection at CaMI Field Research Station, Alberta, Canada
Bibliographic record
Abstract
ABSTRACT For seismic monitoring of geologic CO2 storage, it is useful to measure time-lapse (TL) variations of seismic attenuation. Seismic attenuation directly connects to different petrophysical parameters within the CO2 storage complex. We have developed an approach to derive smooth time-variant amplitude spectra from seismic signals by using the sparse strongest signal peaks, and then measuring two different attributes (conventional Q factor and its “geometric” counterpart) characterizing the path effects of seismic attenuation from the smooth spectra. This approach is straightforward and does not require sophisticated algorithms or parameterization schemes. We apply this approach to TL distributed acoustic sensing (DAS) vertical seismic profile (VSP) data from the Field Research Station (FRS) CO2 injection project in southern Alberta, Canada. High-quality stacked reflection records are obtained from the baseline and monitor DAS VSP surveys at the FRS and TL attenuation-attribute differences are derived from these reflection records. TL variations of attenuation are observed within the injection zone at the FRS, which are interpreted as being related to the injected CO2. Although there is always a significant trade-off between the accuracy and temporal resolution of the measured attenuation parameters, reliable attenuation measurements around the injection zone are still achieved with an in-use reflection signal of a sufficient length and bandwidth. Attenuation attributes measured from this approach can be an advantageous tool for monitoring the distribution and migration of CO2 plumes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".