Field tests of multicomponent seismic optical fiber sensor deployments
Bibliographic record
Abstract
Can we deploy DAS fibers in such a way that we locally sense multiple components of a seismic wavefield, increasing the information derived from a seismic experiment, but at the same time maintain its simplicity, and avoid introducing fundamentally new devices or pre-processing? To answer this, we installed and tested a buried experimental multi-component fiber sensor called the “Pretzel” at the Carbon Management Canada Newell County Facility in 2018, consisting of two 10x10 m horizontal squares of fiber. The 10 m sides of the Pretzel are longer than the 7 m gauge length that we typically use, such that we can be assured of acquiring at least one data trace that is unaffected by the corners of the sensor. The Pretzel is too large to either permit a vertical component or to be considered a point sensor, motivating us to install and test a smaller multi-component fiber sensor that could incorporate a vertical component. Three 1x1x1 m sensors (the “Croissant”) were installed and tested in 2023. Initial results show good comparability to Pretzel data as well as to surface geophone data converted to strain-rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".