Ultra-low frequency dynamic strain detection with laser frequency drifting compensation based on a random fiber grating array
Bibliographic record
Abstract
Dynamic strain sensing over a frequency range from 0.01 to 20 Hz can be used for monitoring earthquakes and volcanoes, charting rock and petroleum formations beneath the earth. However, significant laser frequency drifting (LFD) has limited the detection in this frequency range, especially for distributed frequency detection with phase optical time domain reflectometry (OTDR), where the LFD will introduce a time dependent noise destroying the dynamic strain reconstruction. In this study, a simple and effective method that utilizes the referenced random fiber grating to monitor the variation of laser frequency has been both theoretically analyzed and experimentally demonstrated. During the maximum up to 200 s data acquisition time, the frequency variation of a distributed feedback (DFB) laser with MHz linewidth is obtained from the referenced portion of sensing signal, and then the 1 Hz and 0.01 Hz dynamic strain variations with amplitude of 30 µ ε are reconstructed with strain measurement standard deviation of 66 n ε . Due to signal-to-noise ratio (SNR) enhanced Rayleigh traces from random fiber gratings, a minimum detectable frequency drifting of 7.28 MHz could be achieved over the optical frequency of 2 × 10 14 H z .
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".