MétaCan
Menu
Back to cohort
Record 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 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGeoenvironmental Disasters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsLandslideSeismologyGeologyFault (geology)Earthquake predictionBiogeosciencesRange (aeronautics)Active faultEarth science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.211
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it