Characterising the kinematics of the Joffre Peak landslides using a combined numerical modeling-remote sensing approach
Bibliographic record
Abstract
Geological structure and kinematics are often the most important factors controlling the stability of high rock slopes; their characterization can provide insights that are instrumental in understanding the behaviour of a slope in addition to its evolution with time. In this research, we used a combined remote sensing-numerical modelling approach to characterize the Joffre Peak landslides (British Columbia, Canada), two rock avalanche events that occurred on May, 13th and 16th 2019. The May 13th event involved a volume of 2-3million m3, and resulted in a runout distance of 6 km. The May 16th event involved a volume of 2-3 million m3, and a runout distance of 4 km. The failure was likely promoted by permafrost degradation and reduction in shear strength along geological structures (in our simulation checked in dry condition). Using a wide range of techniques, including Structure-from-Motion photogrammetry, virtual outcrop discontinuity mapping, GIS analysis, and 3D distinct element numerical modelling, we investigated the important role that structural geology and slope kinematics played prior to and during the Joffre landslide events. In particular, we demonstrate that i) a very persistent, sub-vertical geological structures formed the lateral and rear release surfaces of the rock mass volume that failed as two discrete landslide events. The landslide blocks were separated by one such sub-vertical structure, which remains visible in the fresh landslide scar; ii) the first block, failed on May 13th 2019, involving planar sliding failure mechanism, possibly promoted by progressive failure and propagation of discontinuities along the basal surface. The detachment of this block enhanced the kinematic freedom of the second landslide block, which, on May 16th, failed as wedge/toppling mechanism; iii) the first landslide block acted as a key block; its displacement and failure provided the kinematic freedom for the occurrence of the second landslide. In this paper we show that combining remote sensing mapping and 3D numerical modelling allows for the identification of the structural geological features controlling the stability and evolution of high rock slopes in alpine environments. We also show that constraining and validating the numerical modelling results using historical data is of paramount importance to ensure that the correct failure mechanism of the landslides is simulated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".