Algorithms for Tsunami Detection by High Frequency Radar : Development and Case Studies for Tsunami Impact in British Columbia, Canada
Bibliographic record
Abstract
A shore-based High-Frequency (HF) WERA radar was recently installed by Ocean Networks Canada (ONC) near Tofino, British Columbia (Canada), to mitigate the elevated tsunami hazard along the shores of Vancouver Island, from both far- and near-field seismic sources and, in particular, from the Cascadia Subduction Zone (CSZ). With this HF radar, ocean currents can be measured up to a 70-85 km range, depending on atmospheric conditions, based on the Doppler shift they cause in ocean waves at the radar Bragg frequency. In earlier work, the authors (and others) have shown that tsunami currents need to be at least 0.15-0.20 m/s to be reliably detectable by HF radar, when considering environmental noise and background currents (from tide and mesoscale circulation). This would limit the direct detection of tsunami-induced currents to shallow water areas where they are sufficiently strong due to wave shoaling and, hence, to the continental shelf. It follows that, in locations with a narrow shelf, warning times based on such a tsunami detection method may be small. To detect tsunamis in deeper water, beyond the shelf, the authors have proposed a new algorithm that does not require "inverting" currents, but instead is based on spatial correlations of the raw radar signal at two distant locations/cells located along the same wave ray, time shifted by the tsunami propagation time along the ray. A pattern change in these correlations indicates the presence of a tsunami. They validated this algorithm for idealized tsunami wave trains propagating over a simple seafloor geometry in a direction normally incident to shore. Here, this algorithm is further developed, extended, and validated for realistic case studies conducted for seismic tsunami sources and using the bathymetry, offshore of Vancouver Island, BC. Tsunami currents, computed with a state-ofthe-art long wave model, are spatially averaged over cells aligned along individual wave rays, within the radar sweep area, obtained by solving the wave geometric optic equation. A model simulating ONC radar's backscattered signal in space and time, as a function of the simulated tsunami currents, is applied on the Pacific Ocean side of Vancouver Island. Numerical experiments are performed, showing that the proposed algorithm works for detecting a realistic tsunami. Correlation thresholds relevant for tsunami detection can be inferred from the results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".