Improving the Hawaiian Seismic Network for Earthquake Early Warning
Bibliographic record
Abstract
Research Article| February 08, 2017 Improving the Hawaiian Seismic Network for Earthquake Early Warning Alicia J. Hotovec‐Ellis; Alicia J. Hotovec‐Ellis aDepartment of Earth and Space Sciences, University of Washington, Box 351310, Seattle, Washington 98185 U.S.A.ahotovec@uw.edu Search for other works by this author on: GSW Google Scholar Paul Bodin; Paul Bodin aDepartment of Earth and Space Sciences, University of Washington, Box 351310, Seattle, Washington 98185 U.S.A.ahotovec@uw.edu Search for other works by this author on: GSW Google Scholar Wes Thelen; Wes Thelen bU.S. Geological Survey Cascades Volcano Observatory, 1300 Southeast Cardinal Court, Vancouver, Washington 98683 U.S.A. Search for other works by this author on: GSW Google Scholar Paul Okubo; Paul Okubo cU.S. Geological Survey Hawaiian Volcano Observatory, Crater Rim Drive, Hawaii Volcanoes National Park, Hawaii 96718 U.S.A. Search for other works by this author on: GSW Google Scholar John E. Vidale John E. Vidale aDepartment of Earth and Space Sciences, University of Washington, Box 351310, Seattle, Washington 98185 U.S.A.ahotovec@uw.edu Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2017) 88 (2A): 326–334. https://doi.org/10.1785/0220160187 Article history first online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Alicia J. Hotovec‐Ellis, Paul Bodin, Wes Thelen, Paul Okubo, John E. Vidale; Improving the Hawaiian Seismic Network for Earthquake Early Warning. Seismological Research Letters 2017;; 88 (2A): 326–334. doi: https://doi.org/10.1785/0220160187 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The motivation for earthquake early warning (EEW) is the fact that in many applications a few extra seconds of notice ahead of the about‐imminent strong shaking can provide significant benefit. Reducing data latencies, accelerating processing times, and tuning seismic station distributions increase time available for warning. We assess the feasibility of EEW for Hawai‘i and examine how additional stations or upgrades to existing stations can improve warning times. We designed an objective method to identify the most efficient sites for improving an existing seismic network’s coverage, taking both seismic station distribution and seismic hazard into account. The choice of locations for new seismic station sites is informed by improvements in warning time, considering the distribution of seismic hazard and exposure. New sites that improve warning time from earthquakes that are most likely to generate significant ground motions are given preference. This technique may be applied to any seismically active region and target infrastructure in which seismic hazard is spatially defined. We demonstrate this method’s use on the Island of Hawai‘i, with focus on warnings to astronomical observatories on Mauna Kea and island population centers Hilo and Kailua‐Kona. We identified 13 candidate sites for new sensors, telemetry upgrades, or new station installations that should provide an additional 1–4 s of warning for the most probable damaging earthquakes in southern Ka‘ū and northern offshore regions in which 2–14 s and <4 s of warning are currently estimated, respectively. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.029 |
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".