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Record W6992852728

Modeling Tsunamis for Improved Hazard Assessment and Detection

2016· article· en· W6992852728 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Media Literacy Education · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsHazardContext (archaeology)Hazard analysisWarning systemEstuarySubmarineSubmarine pipelineNumerical modelingTsunami wave
DOInot available

Abstract

fetched live from OpenAlex

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.)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.016
GPT teacher head0.287
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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