Toward improved distributed acoustic sensing sensitivity for surface-based reflection seismics: Configuration tests at the Aquistore CO2 storage site
Bibliographic record
Abstract
ABSTRACT Alternative fiber configurations have been tested in an attempt to improve the sensitivity of surface-deployed distributed acoustic sensing (DAS) fiber cables for the purpose of recording steep-angle P-wave reflections. Four alternative fiber configurations were deployed at the Aquistore CO2 storage site to record 401 dynamite shots during a 3D vertical seismic profiling survey. The test cable comprised horizontal configurations (straight fiber, helixes, and asymmetric helixes) buried in a shallow trench and vertical configurations (straight fiber and helixes) deployed in 3.5 m drillholes. Evaluation focused on deep reflections with two-way traveltimes of 0.8–1.8 s. All of the alternative fiber configurations increased the sensitivity relative to the horizontal straight fiber. Sensitivity was highest for the vertical straight fiber configurations and the asymmetric helixes with sensitivity increases of more than 10 and 5 dB, respectively, and amplitude-variation-with-offset behavior similar to that of a vertical-component geophone for reflections with incidence angles of 0°–15° at the surface and 0°–34° at the reflector. Modeling of the DAS responses explains the general pattern of sensitivity variability among the different configurations, but it does not explain the large range of observed sensitivities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".