Validation of DAS data integrity against standard geophones — DAS field test at Aquistore site
Bibliographic record
Abstract
Abstract Distributed acoustic sensing (DAS) using fiber-optic cables is a recent addition to seismic acquisition methods. However, a DAS “sensor” differs significantly from conventional, discrete sensing devices such as geophones or accelerometers. For one, DAS measures something akin to strain instead of particle velocity or acceleration. Other properties of the DAS system also aren't obvious at first. What is its instrument response, noise performance, and repeatability? How are DAS channels properly positioned, e.g., in case of a borehole deployment: depth calibrated? To better understand these issues and their impact on the DAS seismic method's application space, a field test was conducted in which three DAS vendors recorded the same survey using a borehole-installed fiber while recording simultaneously with a conventional downhole array. The results show that all DAS systems achieved good, repeatable signal integrity while exhibiting different noise characteristics. DAS noise can be addressed with well-established processing algorithms, but further benefits can be gained from DAS-specific algorithms. Where required, DAS seismic data can be processed to closely match the vector response of conventional geophones. DAS data converted in this way can assist in the up/down separation step without the need for dip filters. DAS VSP data can also be merged with conventional 3D and 4D seismic, adding value in situations such as undershooting of surface facilities in marine settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".