Physical modeling of landslide generated tsunamis in various scenarios from Fjords to Conical Islands
Bibliographic record
Abstract
Tsunamis generated by landslides and volcanic island collapses account for some of the most catastrophic events, and the largest recorded wave runup was generated by a supercritical landslide impact in Lituya Bay, Alaska. Unfortunately field data from these types of events is very limited, consisting of rare field measurements of landslide scarp, landslide deposit and tsunami runup, as well as eyewitness accounts. Critically important field data related to the landslide motion and tsunami evolution are lacking. Source and runup scenarios based on real world events are physically modeled in the three- dimensional NEES tsunami wave basin at Oregon State University. A unique pneumatic landslide tsunami generator (LTG) was deployed to simulate landslides with varying geometry and kinematics. The LTG consists of a sliding box filled with up to 1,350 kg of naturally rounded river gravel or cobbles which is accelerated by means of four pneumatic pistons down the 2H: 1V slope, launching the granular landslide towards the water at velocities of up to 6 m/s. Topographical and bathymetric features can greatly affect wave characteristics and runup heights. Landslide generated tsunamis were studied in different topographic and bathymetric configurations: basin-wide propagation and runup, a narrow fjord and curved headland configurations, and a conical island setting simulating landslides off an island or a volcanic flank collapse. Water surface elevations were measured using an array of resistance wave gauges. The granular landslide width, thickness and front velocity were measured using above and underwater cameras. Wave runup was measured with a combination of resistance wave gauges and overlapping video recordings calibrated along the slope. The effects from lateral hill slope curvature are analyzed: the leading wave crest amplitude generated on a planar hill slope is 3% larger on average than the leading wave crest generated on a convex conical hill slope, while the leading wave trough and second wave crest amplitudes are smaller. The fjord scenario traps the wave energy resulting in the average maximum runup being 10% larger than in the curved headland scenario, which allows some wave energy to leak into the open basin. Between 1-24% of the landslide kinetic energy is transferred in to the wave train. Cobble landslides transfer on average 43% more kinetic energy into the wave train than the gravel landslide. Predictive equations for the offshore and laterally propagating wave and runup amplitudes, periods, celerities and lengths are derived, which allow an initial rapid tsunami hazard assessment. Finally, the predictive wave and runup equations are applied to the 2007 field event in Chehalis Lake, Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".