Diving-wave time-lapse delay for CO2 thin layer detection
Bibliographic record
Abstract
SUMMARY We have derived an analytical approximate expression to estimate the delay in diving seismic waves due to thin layers of CO2. The expression is valid for high frequencies and can be used to estimate the delay in diving waves at seismic frequencies for large separations between the source and receiver (offset). The approximation may be used to assess CO2 detection limits using diving waves and to support survey planning for CO2 monitoring and full-waveform inversion (FWI) cycle skipping analysis. In this study, we analyse the diving-wave response to a thin layer of CO2 for band-limited data using acoustic finite-difference modelling, and compare the results against the analytical calculations. We find that the responses are offset-dependent and related to double- and single-leg interactions between the diving waves and the CO2. To test the methods, we created a synthetic representation of the 2010 subsurface conditions for the top CO2 layer at the Sleipner storage complex in the North Sea, by combining base and monitor post-stack seismic data with field velocity trends. Using the acoustic finite-difference method, we model pre-stack data that captures the complexity of field data and demonstrate the use of the diving-wave delay for CO2 migration monitoring and CO2 thin layer detection.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 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".