Assessing the landslide failure surface depth and volume: A new spline interpolation method
Bibliographic record
Abstract
Landslides pose significant risk to the communities and infrastructure, particularly in mountainous regions. Accurate estimation of landslide slip surface depth/geometry and volume of displaced material is crucial for hazard assessment, borehole planning and mitigation strategy. This study presents a novel, cost-effective method based on spline interpolation to estimate the depth of slip surface using Digital Elevation Model (DEM) data, especially in areas with limited field data. The method relies on exposed boundary scarps, making it particularly useful for failed slopes with debris cover or well-developed slow-moving landslides where the slip surface is underneath the sliding material. The approach is validated through two case studies in Western Canada, the Hope Slide and the Downie Slide. The results demonstrate that the interpolated slip surface geometry, provided depth estimates and volume distributions that align closely with existing data, with a maximum volume of about 61 million m 3 for the Hope Slide and 0.9 billion m 3 for the Downie Slide. As the method is iterative, the stopping criteria can be decided on meeting a certain slope angle, depth or volume depending on the requirements. Further, the algorithm is flexible to include any additional data related to the slip surface in form of 3D exposure planes or 1D borehole depths. This was tested on the case studies by providing additional data and showing improved results on estimating the final slip surface geometry. Beyond landslides analysis the method can be applied for topographic corrections and removing deposited material from other surface processes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".