Fibre Optic Methods of Prospecting: A Comprehensive and Modern Branch of Geophysics
Bibliographic record
Abstract
Abstract Over the past decades, the development of fibre optic cables, which pass light waves carrying data guided by total internal reflection, has led to advances in high-speed and long-distance communication, large data transmission, optical imaging, and sensing applications. Thus far, fibre optic sensors (FOSs) have primarily been employed in engineering, biomedicine, and basic sciences, with few reports of their usage in geophysics as point and distributed sensors. This work aimed at reviewing the studies on the use of FOSs in geophysical applications with their fundamental principles and technological improvements. FOSs based on Rayleigh, Brillouin, and Raman scatterings and fibre Bragg grating sensors are reviewed based on their sensing performance comprising sensing range, spatial resolution, and measurement parameters. The recent progress in applying distributed FOSs to detect acoustic, temperature, pressure, and strain changes, as either single or multiple parameters simultaneously on surface and borehole survey environments with their cable deployment techniques, has been systematically reviewed. Despite the development of fibre optic sensor technology and corresponding experimental reports of applications in geophysics, there have not been attempts to summarise and synthesise fibre optic methods for prospecting as a comprehensive and modern branch of geophysics. Therefore, this paper outlines the fibre optic prospecting methods, with an emphasis on their advantages, as a guide for the geophysical community. The potential of the new outlined fibre optic prospecting methods to revolutionise conventional geophysical approaches is discussed. Finally, the future challenges and limitations of the new prospecting methods for geophysical applications are elucidated.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".