Detecting small gravity change in field measurement: simulations and experiments of the superconducting gravimeter—iGrav
Bibliographic record
Abstract
In order to detect small gravity changes in field measurements, such as with CO2 storage, we designed simulations and experiments to validate the capabilities of the iGrav superconducting gravimeter. Qualified data processing was important to obtain the residual gravity from the iGrav's raw gravity signals, without the tidal components, atmosphere, polar motion and hydrological effects. Two simulations and four designed experiments are presented in this study. The first simulation detected the gravity change during CO2 injection. The residual gravity of CO2 leakage was targeted with the second simulation from the main storage reservoir to secondary space underground. The designed experiments monitored the situation of gravity anomalies in the iGrav's records. These tests focused on short-term gravity anomalies, such as gravity changes, step functions, repeat observations and gradient measurements from the iGrav, rather than on long-term tidal effects. The four laboratory experiments detected a decrease in gravity of -0.56 ± 0.15 µGal (10-8 m s-2) with a 92.8 kg weight on the top of the iGrav. A step function occurred in the gravity signals, when the tilt control was out of balance. We also used a professional camera dolly with a track to observe repeated horizontal movements and an electric lift table for controlled vertical movements to measure the average gradient of -2.67 ± 0.01 µGal cm-1.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".