Semi-empirical predictive equations for the initial amplitude of submarine landslide-generated waves: applications to 1994 Skagway and 1998 Papua New Guinea tsunamis
Bibliographic record
Abstract
Abstract Accurate predictions of maximum initial wave amplitude are essential for coastal impact assessment of tsunami waves generated by submarine landslides. Here, we analyse the existing predictive equations for the maximum initial amplitude ( $$ \eta_{\text{max} } $$ ηmax ) of submarine landslide-generated waves and study their performance in reproducing real-world landslide incidents. Existing equations include various landslide parameters such as specific gravity (γs), initial submergence (d), slide length (B), width (w), thickness (T) and slope angle (θ). To determine how landslide parameters affect wave amplitude, we conduct a systematic sensitivity analysis. Results indicate that the slide volume (V = B × w × T) anddare among the most sensitive parameters. The data from the 1994 Skagway (observed $$ \eta_{\text{max} } $$ ηmax : 1.0–2.0 m) and 1998 Papua New Guinea (PNG) (observed $$ \eta_{\text{max} } $$ ηmax : 10–16 m) incidents provided valuable benchmarks for evaluating the performance of the existing equations. The predicted maximum initial amplitudes of 0.03–686.5 m and 3.7–6746.0 m were obtained for the 1994 and 1998 events, respectively, indicating a wide range for wave amplitudes. The predicted estimates for the smaller-sized event, i.e. the 1994 Skagway, appear to be more accurate than those made for the larger event, i.e. the 1998 PNG case. We develop a new predictive equation by fitting an equation to actual submarine landslide tsunamis: $$ \eta_{ \text{max} } = 50.67 \left( {\frac{V}{d}} \right)^{0.34} $$ ηmax=50.67Vd0.34 , whereVis the slide volume (km3),dis initial submergence depth (m), and $$ \eta_{\text{max} } $$ ηmax is in metres. Our new equation gives wave amplitudes of 1.6 m and 7.8 m for the 1994 and 1998 landslide tsunamis, respectively, which are fairly consistent with real observations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".