MétaCan
Menu
Retour à la cohorte
Enregistrement W4290960230 · doi:10.55461/rdwc6463

Data Resources for NGA-Subduction Project

2020· paratext· en· W4290960230 sur OpenAlexaboutno aff
Yousef Bozorgnia, Jonathan P. Stewart

Notice bibliographique

RevuePEER · 2020
Typeparatext
Langueen
DomaineEarth and Planetary Sciences
ThématiqueSeismic Waves and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSubductionMetadataGeologySeismologyTectonicsSeismic hazardDatabaseData fileComputer scienceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

The NGA-Subduction (NGA-Sub) project is one in a series of Next Generation Attenuation (NGA) projects directed towards database and ground-motion model development for applications in seismic-demand characterization. Whereas prior projects had targeted shallow crustal earthquakes, active tectonic regions (NGA-West1 and NGA-West2), and stable continental regions (NGA-East), NGA-Sub is the first to address specifically subduction zones, which are a dominant source of seismic hazard in many regions globally, including the Pacific Northwest region of the United States and Canada. This report describes the development of data resources for the NGA-Sub project. Agreements were formed with many owners and providers of ground-motion data and metadata worldwide to support data collection. Prior NGA projects organized the data collected into a series of spreadsheets. The enormous amount of the collected data for NGA-Sub required abandoning that strategy and ultimately the data was organized into a relational database consisting of 23 tables containing various data, metadata, and outputs of various codes required to compute desired quantities (e.g., intensity measures, distances, etc.). A schema was developed to relate fields in tables to each other through a series of primary and foreign keys. As with prior NGA projects, model developers and others largely interact with the data through flatfiles specific to certain types of intensity measures (e.g., pseudo-spectral accelerations at a certain oscillator damping level); such flatfiles are a time-stamped output of the database. The NGA-Sub database contains 70,107 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Component-specific usable period ranges are identified. Various ground-motion intensity measures (IMs) were computed including peak acceleration, peak velocity, pseudo-spectral accelerations for a range of oscillator periods and damping ratios, Fourier amplitudes, Arias intensity, significant durations, and cumulative absolute velocity-parameters. Source parameters were assigned for earthquakes that produced recordings. Some of the 1880 earthquakes were screened out because of missing magnitudes or hypocenter locations, which decreased the number of potentially usable earthquakes to 1782. Further screening to remove events without an assigned event type (e.g., interface, intraslab, etc.) or distances reduced the number of events to 976. For those 976 events, source parameters of two general types are assigned: those related to the focus (including moment tensors) and those related to finite-fault representations of the source. A series of source-to-recording site distances and other parameters are provided using finite-fault representations of seismic sources. Finite-fault models of sources were developed from literature where available and from a simulation procedure otherwise. As part of the NGA-Sub project, the simulation procedure was revised and more fully documented. In addition, all events are reviewed to assign event types, event classes (mainshock, aftershock, etc.), and event locations relative to volcanic arcs. Quality assurance (QA) of ground-motion data and source/path metadata was an important component of NGA-Sub. For ground motions, QA procedures included visual checks of records prior to processing, checks of records from each network that recorded each earthquake to check for systematic outliers (perhaps indicative of gain problems), and checks of limiting distances beyond which data sampling for a given event is likely to be biased by data approaching noise thresholds. Source/path QA procedures largely involved checking that information in database fields accurately reflects source documents. Site metadata was compiled into a site table containing time-averaged shear-wave velocities in the upper 30 m of sites (VS30), basin depths, and related uncertainties. Major efforts were undertaken during the project to develop shear-wave velocity profile databases and to use those data to develop regional predictive models for site parameters when site-specific measurements are unavailable. Many of those predictive relations were published in journal or conference papers over the course of the NGA-Sub project (i.e., for Alaska, Cascadia, Chile, and Taiwan); those results are reviewed only briefly. Rather, emphasis in this report has been placed on procedures used for other regions. In addition to site parameters, all sites are also assigned a location relative to local volcanic arcs.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,150
Score d'incertitude au seuil0,502

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,023
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,012
Études des sciences et des technologies0,0010,001
Communication savante0,0050,005
Science ouverte0,0050,005
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,1500,159

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.

Tête enseignante Opus0,095
Tête enseignante GPT0,302
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations55
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuePEERMême sujetSeismic Waves and AnalysisTravaux en français237 207