Low-Noise Optical Accelerometers: Bridging the Gaps among Geophones, Accelerometers, and Broadbands in a Deep Borehole
Bibliographic record
Abstract
Abstract Earthquakes and other seismic sources produce waves with frequency content spanning many orders of magnitude. Recording a broader frequency band of interest has historically required deploying multiple instruments designed to work the best within limited, overlapping frequency ranges. Here, we detail a 300 m deep borehole deployment of a sensor package, including three new optical accelerometers that can potentially replace many dedicated instruments with a single, low-noise sensor. These instruments are designed with a flat frequency response from 0.005 to 1500 Hz, spanning the flat response segments of broadband sensors and geophones, as well as a low-noise floor and high sensitivity. The sensors have been functioning normally for over four years, fully grouted at depths of >100 m. Year-long power spectral density (PSD) profiles show that the optical accelerometers have a lower noise floor than a colocated geophone for all frequencies, with 20 dB noise reduction at 250 Hz. PSD comparisons to a broadband sensor installed at the surface show a 5–30 dB noise reduction for the optical accelerometer above 1 Hz, although this is likely due, in part, to the broadband sensor being subjected to much higher surface noise. At periods >5 s, the broadband sensor shows up to 20 dB lower noise than the optical accelerometer, which in turn has up to 50 dB lower noise floor than the colocated geophone. Finally, modeling the Brune displacement spectrum for theoretical seismicity within 1 km of the borehole shows that the optical accelerometers could potentially deliver a detection threshold improvement of one magnitude unit relative to the colocated geophone.
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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 | 0.000 | 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".