Examining the Feasibility of Sentinel-1 InSAR data for landslide monitoring and failure forecasting in western Canada.
Bibliographic record
Abstract
Landslides are geological hazards that significantly threaten human life, infrastructure, and biotic habitat in areas with steep slopes. Precursory signs of a landslide can be undetectable or non- existent, making the evacuation of residents unlikely. The ongoing climatic cycles and geological triggers imposed on regions susceptible to landslides exhibit long-term ground movement superimposed with accelerations due to seismicity or precipitation. Landslide monitoring and forecasting aims to understand the structural dynamics of the slide accelerations to estimate when there will be a catastrophic failure. The thesis explores the potential of InSAR technology for monitoring slope movement in the western Canadian Cordillera. The study takes a two- pronged approach: first, investigating the capability of the technique to detect movement on slopes that have already undergone previous landslide activity, focussing on the Garibaldi Volcanic Complex (GVC) as a case study, and second, analyzing five sites that have recently experienced landslides to determine if InSAR technology could have forecasted the failures. The InSAR results presented in the thesis show that ground displacement occurred on the slopes of all the study sites, which corresponded with previous landslide activity. However, InSAR results collected during winter months were less detailed and frequent than those collected using a seasonal approach. The forecasting study discovered that all the sites displayed signs of preceding movement on the slopes, which were successfully detected by InSAR. Furthermore, each site encountered extreme weather conditions, resulting in catastrophic failure. The Elliot Lake and Ecstall River sites experienced seismic activity the same afternoon as the landslide events, potentially connected to glacial loss and retreat. Results obtained during snowfall were less reliable than the summer acquisitions. iv The results from the thesis demonstrate that the Sentinel-1 mission's temporal resolution is inadequate for creating a real-time monitoring system for landslide-prone slopes in western Canada. Factors that trigger landslide acceleration, such as precipitation, seismicity, and geological processes, can occur over decades or hours. Hence, the primary role of Sentinel-1 in landslide monitoring is identifying large-scale moving slopes. Future InSAR platforms could provide a promising solution with high temporal resolution, making landslide forecasting and monitoring a reality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".