Design and deployment of a prototype multicomponent distributed acoustic sensing loop array
Bibliographic record
Abstract
In 2016-2017 a range of analyses and applications of a geometrical model of fibre-optic (DAS) data for arbitrary fibre shapes was described. Amongst those applications was a multicomponent estimation scheme based on a careful accounting, and combined useage, of the varying fibre directions associated with a shaped cable layout. In 2018 a prototype shaped DAS fibre array was buried at the Containment and Monitoring Institute Field Research Station in Newell County AB to put some of these ideas and their feasibility to the test. The loop was illuminated from several directions, and shot records were analyzed to assess if directional sensitivity is sufficient to permit multiple components of strain to be estimated simultaneously. By picking a P-wave arrival and comparing it to an analytic model, we conclude with a cautious “yes”. The size of the loop (roughly 10 m on a side) was chosen to accommodate standard DAS gauge lengths; at this size, horizontal but not vertical strain rate components were sensed. Future versions designed for gauge lengths on the order of 2 m will permit 6-component estimation. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 2:15 PM Location: 221C Presentation Type: Oral
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".