Improving the Hawaiian Seismic Network for Earthquake Early Warning
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,007 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».