Modeling Tsunamis for Improved Hazard Assessment and Detection
Why this work is in the frame
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Bibliographic record
Abstract
This body of work uses state of the art numerical models to assess and reduce tsunami hazard. The first manuscript describes the use of these models to explore nonlinear interaction between tide and tsunami in the context of hazard assessment. Inundation due to several probable maximum tsunamis (PMTs) is considered in the Hudson River Estuary (HRE). Of the sources considered, a submarine mass failure (SMF) poses the most significant tsunami threat in this region and across the entire US East Coast. The next manuscript focuses on the how SMF mechanics effect tsunami generation. In addition to inundation, SMF tsunamis are dangerous because of their short or nonexistent warning times. The final manuscript discusses developments to an algorithm which extend the range of tsunami detection by shore-based HF radar, thereby increasing warning times. Tsunami hazard assessment in the Hudson River Estuary based on dynamic tsunami-tide simulation. The first manuscript is part of a tsunami inundation mapping activity carried out along the US East Coast (USEC) since 2010, under the auspice of the National Tsunami Hazard Mitigation program (NTHMP). Two densely built low-lying regions are situated along this coast: Chesapeake Bay and HRE. HRE is the object of this work, with specific focus on assessing tsunami hazard in Manhattan, the Hudson River and East River areas. Modeling coastal tsunami hazard from submarine mass failures: effect of slide rheology, experimental validation, and case studies off the US East coast. We first validate two models simulating tsunami generation by deforming submarine mass failures (SMFs) against laboratory experiments for SMF made of glass beads moving down a steep slope. These are two-layer models, in which the upper layer is water, simulated with the non-hydrostatic 3D non-hydrostatic model NHWAVE, and the SMF bottom layer is simulated with depth-integrated equations and represented either as a dense Newtonian fluid or a granular medium. At most nearshore locations surface elevations caused by the rigid slump are significantly larger (up to a factor of 2) than those caused by the 3 deforming slides. Hence, the rigid slump provides a conservative estimate of SMF tsunami impact in terms of maximum inundation/runup at the coast, while using a more realistic rheology with some level of SMF deformation, in general, leads to a reduced tsunami impact at the coast. This validates as conservative the tsunami hazard assessment and inundation mapping performed to date as part of NTHMP, on the basis of Currituck SMF proxies simulated as rigid slump. Algorithms for tsunami detection by High Frequency Radar : development and case studies for tsunami impact in British Columbia, Canada. To mitigate the tsunami hazard along the shores of Vancouver Island in British Columbia (Canada), Ocean Networks Canada (ONC) has been developing a Tsunami Early Warning System (TEWS), combining instruments (seismometers, pressure sensors) deployed on the sea floor as part of their Neptune Observatory, and a shore-based High-Frequency (HF) radar. The authors have proposed a new detection algorithm based on spatial correlations of the raw radar signal at two distant locations along the same wave ray. In a previous work, they validated this algorithm for idealized tsunami wave trains propagating over a simple sea floor geometry in a direction normally incident to shore. In the final manuscript, this algorithm is extended and validated for realistic tsunami case studies conducted for seismic sources and using the bathymetry off of Vancouver Island, BC. (Abstract shortened by ProQuest.)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it