High Fidelity Distributed Fiber Optic Sensing for Landslide Detection
Bibliographic record
Abstract
Abstract Pipeline integrity management continues to adapt and improve with the adoption of new technologies. In 2016, Husky Midstream had a loss of containment incident on a 16-inch diameter pipeline on the south slope of the North Saskatchewan River in Saskatchewan, Canada. The incident was determined to be caused by ground movement resulting from a landslide complex. The landslide complex on the south slope contains two deep-seated compound basal shear slides as well as a near surface translational slide in low strength clay shales. A robust state-of-the-art instrumentation monitoring program was implemented that included real-time geotechnical instrumentation, high fidelity distributed fiber optic sensing (HDS), repeat ILI and on-site weather data to identify, evaluate and monitor areas of ground and pipeline movement so that potential impacts to the pipelines could be mitigated. An early-warning system that included alarm thresholds was developed that identified when to proactively shut-in the pipeline. The HDS monitoring comprised strain, acoustic and temperature sensing, and was installed to provide leak detection monitoring, however, it revealed an excellent correlation to the geotechnical, ILI and weather station monitoring data on the actively moving landslide complex. This paper shows a number of these correlations over the HDS monitoring period from October 2017 to November 2018. The HDS monitoring showed increased strain magnitudes following significant rainfall events that correlated to an acceleration in slope inclinometer (SIs fitted with shape accelerometer arrays, SAAs) and survey monument (SM) movement. Locations of high strain accumulation correlated to ILI locations of bending strain. Accumulated strain and displacements in SAAs and SMs were measured despite the lack of visual evidence of ground cracking on the right-of-way (ROW). Construction activity on the ROW was detected through acoustic and strain signatures. As pipeline operators continue to include high fidelity fiber optic sensing as a continuous linear sensor along new and existing pipelines primarily as a leak detection tool, other critical applications, such as early detection of active landslides can be included as an important component of the integrity management system or separately as part of a robust monitoring system.
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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".