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Enregistrement W2377820877 · doi:10.1186/s40677-016-0041-1

A new classification of earthquake-induced landslide event sizes based on seismotectonic, topographic, climatic and geologic factors

2016· article· en· W2377820877 sur OpenAlex

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Notice bibliographique

RevueGeoenvironmental Disasters · 2016
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueLandslides and related hazards
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLandslideSeismologyGeologyFault (geology)Earthquake predictionBiogeosciencesRange (aeronautics)Active faultEarth science

Résumé

récupéré en direct d'OpenAlex

This paper reviews the classical and some particular factors contributing to earthquake-triggered landslide activity. This analysis should help predict more accurately landslide event sizes, both in terms of potential numbers and affected area. It also highlights that some occurrences, especially those very far from the hypocentre/activated fault, cannot be predicted by state-of-the-art methods. Particular attention will be paid to the effects of deep focal earthquakes in Central Asia and to other extremely distant landslide activations in other regions of the world (e.g. Saguenay earthquake 1988, Canada). The classification of seismically induced landslides and the related ‘event sizes’ is based on five main factors: ‘Intensity’, ‘Fault factor’, ‘Topographic energy’, ‘Climatic background conditions’, ‘Lithological factor’. Most of these data were extracted from papers, but topographic inputs were checked by analyzing the affected region in Google Earth. The combination and relative weight of the factors was tested through comparison with well documented events and complemented by our studies of earthquake-triggered landslides in Central Asia. The highest relative weight (6) was attributed to the ‘Fault factor’; the other factors all received a smaller relative weight (2–4). The high weight of the ‘Fault factor’ (based on the location in/outside the mountain range, the fault type and length) is strongly constrained by the importance of the Wenchuan earthquake that, for example, triggered far more landslides in 2008 than the Nepal earthquake in 2015: the main difference is that the fault activated by the Wenchuan earthquake created an extensive surface rupture within the Longmenshan Range marked by a very high topographic energy while the one activated by the Nepal earthquake ruptured the surface in the frontal part of the Himalayas where the slopes are less steep and high. Finally, the calibrated factor combination was applied to almost 100 other earthquake events for which some landslide information was available. This comparison revealed the ability of the classification to provide a reasonable estimate of the number of triggered landslides and of the size of the affected area. According to this prediction, the most severe earthquake-triggered landslide event of the last one hundred years would actually be the Wenchuan earthquake in 2008 followed by the 1950 Assam earthquake in India – considering that the dominating role of the Wenchuan earthquake data (including the availability of a complete landslide inventory) for the weighting of the factors strongly influences and may even bias this result. The strongest landslide impacts on human life in recent history were caused by the Haiyuan-Gansu earthquake in 1920 – ranked as third most severe event according to our classification: its size is due to a combination of high shaking intensity, an important ‘Fault factor’ and the extreme susceptibility of the regional loess cover to slope failure, while the surface morphology of the affected area is much smoother than the one affected by the Wenchuan 2008 or the Nepal 2015 earthquakes. The main goal of the classification of earthquake-triggered landslide events is to help improve total seismic hazard assessment over short and longer terms. Considering the general performance of the classification-prediction, it can be seen that the prediction either fits or overestimates the known/observed number of triggered landslides for a series of earthquakes, while it often underestimates the size of the affected area. For several events (especially the older ones), the overestimation of the number of landslides can be partly explained by the incompleteness of the published catalogues. The underestimation of the extension of the area, however, is real – as some particularities cannot be taken into account by such a general approach: notably, we used the same seismic intensity attenuation for all events, while attenuation laws are dependent on regional tectonic and geological conditions. In this regard, it is likely that the far-distant triggering of landslides, e.g., by the 1988 Saguenay earthquake (and the related extreme extension of affected area) is due to a very low attenuation of seismic energy within the North American plate. Far-distant triggering of landslides in Central Asia can be explained by the susceptibility of slopes covered by thick soft soils to failure under the effect of low-frequency shaking induced by distant earthquakes, especially by the deep focal earthquakes in the Pamir – Hindukush seismic region. Such deep focal and high magnitude (> > 7) earthquakes are also found in Europe, first of all in the Vrancea region (Romania). For this area as well as for the South Tien Shan we computed possible landslide event sizes related to some future earthquake scenarios.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,149
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,211
Écart entre enseignants0,200 · 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