A remote sensing approach for the derivation of numerical modelling input data: insights from the Hope Slide, Canada
Bibliographic record
Abstract
In this paper, we describe an integrated remote sensing approach for the collection of geomechanical data to be used as input for continuum, discontinuum, and hybrid numerical analyses. Ground-based and aerial remote sensing techniques, including terrestrial digital photogrammetry (TDP), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and terrestrial infrared thermography (IRT) may be used for collecting rock mass data appropriate for input into varied numerical modelling approaches. To demonstrate our suggested approach, we have used the 1965 Hope Slide, British Columbia, Canada. We present the mapping of rock discontinuities for numerical modelling using a hierarchical geological structure order. Large-scale geological structures which were identified and mapped on the pre-failure and present-day topography are used in a preliminary analysis of the rock slope to investigate their influence on kinematic freedom and in bounding keyblocks. Detailed geomechanical mapping is performed on three-dimensional TDP models. IRT data is used to characterize surface water seepage. Unmanned aerial vehicle (UAV) SfM imagery of the landslide debris was used to analyse the block size distribution. Preliminary numerical discontinuum 3D-DEM modelling based on this data and assigned mechanical properties shows that with detailed planning and systematic field data collection techniques, the geological engineer can obtain the data necessary to reduce both model and parameter uncertainty and allow more reliable and realistic numerical slope simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".