Earthquake ground motion and patterns of seismically induced landsliding
Bibliographic record
Abstract
Earthquake strong ground motion changes stresses in hillslopes and reduces the strength of surface materials. This can cause landsliding during earthquakes and enhance rates of slope failure in epicentral areas for longer periods. Rates of earthquake-triggered landsliding are strongly correlated with measured peak ground acceleration. Patterns of landslide density reflect the attenuation of seismic waves and geologic and topographic site effects. Using historic thrust fault ruptures with well-documented ground motion and landslide distributions as examples, we illustrate the links between earthquake mechanisms, seismic wave propagation, and triggered landsliding. The examples have shared geomorphic attributes: a maximum density of triggered landslides where earthquake slip is greatest; a progressive decrease of landslide density away from this maximum; clustering of triggered landslides on topographic ridges and other convex landscape elements; and preferential failure of slopes facing away from the earthquake source. We also show that rates of landsliding can remain high after an earthquake in a geomorphic crisis that fades over a period of years. Continued landsliding adds to the total erosion caused by an earthquake, reducing or possibly canceling seismic surface uplift. The examples underline the potential for the quantitative prediction of patterns of seismically triggered and induced landsliding, use of observed landslide patterns for study of earthquake mechanisms, and inclusion of seismically driven erosion in landscape evolution models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".