Investigation of the effects of surrounding media on the distributed acoustic sensing of a helically wound fibre-optic cable with application to the New Afton deposit, British Columbia
Bibliographic record
Abstract
Abstract. Fibre-optic sensing technology has recently become popular for oil and gas extraction, mining, geotechnical engineering, and hydrogeology applications. With a successful track record in many applications, distributed acoustic sensing using straight fibre-optic cables has become a method of choice for seismic studies. However, distributed acoustic sensing using straight fibre-optic cables cannot detect off-axial strain at high incident angles (the angle between the ray and normal vector of the surface); hence, a helically wound cable design was introduced to overcome this limitation. The helically wound cable field data at the New Afton deposit in British Columbia, Canada, showed that the quality of the data is highly dependent on the incident angle and surrounding media. A 3D finite element model developed using COMSOL Multiphysics quickly and efficiently assessed the effects of various materials surrounding a helically wound cable for simple geometry for scenarios corresponding to a real deployment of such cable underground at the New Afton mine. The proposed numerical modelling workflow could be applied to more complicated scenarios (e.g., non-linear material constitutive behaviour and the effects of pore fluids). The results of this paper can be used as a guideline for analyzing the impact of surrounding media and incident angle on the response of helically wound cable, optimizing the installation of helically wound cable in various conditions, and validating boundary conditions of 3D numerical models built for analyzing complex scenarios.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".