Insights on Multistage Rock Avalanche Behavior From Runout Modeling Constrained by Seismic Inversions
Bibliographic record
Abstract
Abstract Inversion of low‐frequency regional seismic records to solve for a time series of bulk forces exerted on the earth by a landslide (a force‐time function) is increasingly being used to infer volumes and dynamics of large, highly energetic landslides, such as rock avalanches and flowslides, and to provide calibration information on event dynamics and volumes for numerical landslide runout models. Much of the work to date using landslide runout modeling constrained by seismic data has focused on using single‐phase models with frictional or velocity‐weakening rheologies. Awareness of multistage landslide initiations is increasing, with discrete failures separated in time contributing to the final impact of an event. Our work utilizes a method for incorporating seismic data as a calibration constraint for landslide runout models, considering variable rheologies and different initiation conditions. This study presents a systematic examination of multiple rheologies and initiation conditions, and shows how these factors affect the force‐time function derived from the landslide runout model. Our work confirms that, while rheology and fragmenting or initially coherent initiations affect the force‐time function, multiple collapses separated by tens of seconds have the greatest impact on the shape and amplitude. We apply this method to the analysis of three real rock avalanches to better constrain plausible initiation conditions and rheology parameters using both seismic and field data. This study provides insights on how assumptions about the initiation dynamics of the source zone and the runout model definition can aid in the interpretation of seismic inversions for multistage rock avalanches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".